rsna abdominal-trauma-detection.ipynb

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    "# <div style=\"color:white;display:inline-block;border-radius:5px;background-color:#FF7F50;font-family:Nexa;overflow:hidden\"><p style=\"padding:20px;color:white;overflow:hidden;font-size:80%;letter-spacing:0.5px;margin:0\"><b> </b> Radiological Society of North America (RSNA) 2023 Abdominal Trauma Detection Challenge</p></div>\n",
    "\n",
    "#### Introduction\n",
    "Traumatic injury is the most common cause of death in the first four decades of life and a major  public health problem around the world. There are estimated to be around 5 million annual deaths worldwide from traumatic injury. Prompt and accurate diagnosis of traumatic injury is crucial for initiating appropriate and timely interventions, which can significantly improve patient outcomes and survival rates. Computed Tomography (CT) has become an indispensable tool in evaluating patients with suspected abdominal injuries due to its ability to provide detailed cross-sectional images of the abdomen.\n",
    "\n",
    "Abdominal trauma often cannot be diagnosed clinically by physical exam, patient symptoms, or laboratory tests. Interpreting CT scans for abdominal trauma, however, can be a complex and time-consuming task, especially when multiple injuries or areas of subtle active bleeding are present.\n",
    "\n",
    "The goal of the competition is to identify several potential injuries in CT scans of trauma patients. Any of these injuries can be fatal on a short time frame if untreated so there is great value in rapid diagnosis.\n",
    "\n",
    "#### Common terms\n",
    "- **bowel_injury** - Damage or harm that occurs to the intestines.\n",
    "- **bowel_healthy** - No damage or harm to the intestines.\n",
    "- **extravasation_injury** - Refers to internal bleeding.\n",
    "- **extravasation_healthy** - No internal bleeding detected.\n",
    "- **kidney_healthy** - No kidney injury detected\n",
    "- **kidney_low** - Low grade Kidney injury detected\n",
    "- **kidney_high** - High grade Kidney injury detected\n",
    "- **liver_healthy** - No liver injury detected\n",
    "- **liver_low** - Low grade liver injury detected\n",
    "- **liver_high** - High grade liver injury detected\n",
    "- **spleen_healthy** - No spleen injury detected\n",
    "- **spleen_low** - Low grade spleen injury detected\n",
    "- **spleen_high** - High grade spleen injury detected"
   ]
  },
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   "source": [
    "# <div style=\"color:white;display:inline-block;border-radius:5px;background-color:#FF7F50;font-family:Nexa;overflow:hidden\"><p style=\"padding:20px;color:white;overflow:hidden;font-size:80%;letter-spacing:0.5px;margin:0\"><b> </b>Installing & Importing Modules</p></div>"
   ]
  },
  {
   "cell_type": "code",
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   "id": "494e42e6",
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     "text": [
      "PRETTY_NAME=\"Ubuntu 22.04.2 LTS\"\r\n",
      "NAME=\"Ubuntu\"\r\n",
      "VERSION_ID=\"22.04\"\r\n",
      "VERSION=\"22.04.2 LTS (Jammy Jellyfish)\"\r\n",
      "VERSION_CODENAME=jammy\r\n",
      "ID=ubuntu\r\n",
      "ID_LIKE=debian\r\n",
      "HOME_URL=\"https://www.ubuntu.com/\"\r\n",
      "SUPPORT_URL=\"https://help.ubuntu.com/\"\r\n",
      "BUG_REPORT_URL=\"https://bugs.launchpad.net/ubuntu/\"\r\n",
      "PRIVACY_POLICY_URL=\"https://www.ubuntu.com/legal/terms-and-policies/privacy-policy\"\r\n",
      "UBUNTU_CODENAME=jammy\r\n"
     ]
    }
   ],
   "source": [
    "!cat /etc/os-release"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f81c61c4",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('64bit', '')\n"
     ]
    }
   ],
   "source": [
    "import platform\n",
    "print(platform.architecture())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d94e9a53",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing ./modules/flit_core-3.9.0-py3-none-any.whl\r\n",
      "flit-core is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\r\n",
      "Processing ./modules/wheel-0.41.2-py3-none-any.whl\r\n",
      "Installing collected packages: wheel\r\n",
      "  Attempting uninstall: wheel\r\n",
      "    Found existing installation: wheel 0.40.0\r\n",
      "    Uninstalling wheel-0.40.0:\r\n",
      "      Successfully uninstalled wheel-0.40.0\r\n",
      "Successfully installed wheel-0.41.2\r\n",
      "Processing ./modules/monai-1.2.0-202306081546-py3-none-any.whl\r\n",
      "Requirement already satisfied: torch>=1.9 in /opt/conda/lib/python3.10/site-packages (from monai==1.2.0) (2.0.0)\r\n",
      "Requirement already satisfied: numpy>=1.20 in /opt/conda/lib/python3.10/site-packages (from monai==1.2.0) (1.23.5)\r\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch>=1.9->monai==1.2.0) (3.12.2)\r\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch>=1.9->monai==1.2.0) (4.6.3)\r\n",
      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.9->monai==1.2.0) (1.12)\r\n",
      "Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.9->monai==1.2.0) (3.1)\r\n",
      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.9->monai==1.2.0) (3.1.2)\r\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.9->monai==1.2.0) (2.1.3)\r\n",
      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.9->monai==1.2.0) (1.3.0)\r\n",
      "Installing collected packages: monai\r\n",
      "Successfully installed monai-1.2.0\r\n",
      "Processing ./modules/python_gdcm-3.0.22-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\r\n",
      "Installing collected packages: python-gdcm\r\n",
      "Successfully installed python-gdcm-3.0.22\r\n",
      "Processing ./modules/dicom2nifti-2.4.8-py3-none-any.whl\r\n",
      "Requirement already satisfied: nibabel in /opt/conda/lib/python3.10/site-packages (from dicom2nifti==2.4.8) (5.1.0)\r\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from dicom2nifti==2.4.8) (1.23.5)\r\n",
      "Requirement already satisfied: scipy in /opt/conda/lib/python3.10/site-packages (from dicom2nifti==2.4.8) (1.11.1)\r\n",
      "Requirement already satisfied: pydicom>=2.2.0 in /opt/conda/lib/python3.10/site-packages (from dicom2nifti==2.4.8) (2.4.1)\r\n",
      "Requirement already satisfied: python-gdcm in /opt/conda/lib/python3.10/site-packages (from dicom2nifti==2.4.8) (3.0.22)\r\n",
      "Requirement already satisfied: packaging>=17 in /opt/conda/lib/python3.10/site-packages (from nibabel->dicom2nifti==2.4.8) (21.3)\r\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=17->nibabel->dicom2nifti==2.4.8) (3.0.9)\r\n",
      "Installing collected packages: dicom2nifti\r\n",
      "Successfully installed dicom2nifti-2.4.8\r\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import shutil\n",
    "\n",
    "shutil.copytree('/kaggle/input/modules', '/kaggle/working/modules')\n",
    "!pip install /kaggle/working/modules/flit_core-3.9.0-py3-none-any.whl\n",
    "!pip install /kaggle/working/modules/wheel-0.41.2-py3-none-any.whl\n",
    "!pip install /kaggle/working/modules/monai-1.2.0-202306081546-py3-none-any.whl\n",
    "!pip install /kaggle/working/modules/python_gdcm-3.0.22-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n",
    "!pip install /kaggle/working/modules/dicom2nifti-2.4.8-py3-none-any.whl\n",
    "\n",
    "shutil.rmtree('/kaggle/working/modules')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fa4e52cd",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 1.2.0\n",
      "Numpy version: 1.23.5\n",
      "Pytorch version: 2.0.0\n",
      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
      "MONAI rev id: c33f1ba588ee00229a309000e888f9817b4f1934\n",
      "MONAI __file__: /opt/conda/lib/python3.10/site-packages/monai/__init__.py\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: 0.4.12\n",
      "ITK version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "Nibabel version: 5.1.0\n",
      "scikit-image version: 0.21.0\n",
      "Pillow version: 9.5.0\n",
      "Tensorboard version: 2.12.3\n",
      "gdown version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "TorchVision version: 0.15.1\n",
      "tqdm version: 4.65.0\n",
      "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "psutil version: 5.9.3\n",
      "pandas version: 1.5.3\n",
      "einops version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "transformers version: 4.30.2\n",
      "mlflow version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "pynrrd version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "\n",
      "For details about installing the optional dependencies, please visit:\n",
      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "import torchvision\n",
    "import os\n",
    "import sys\n",
    "import shutil\n",
    "import tempfile\n",
    "import pydicom\n",
    "from glob import glob\n",
    "import dicom2nifti\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import nibabel as nib\n",
    "import monai\n",
    "from nilearn import plotting\n",
    "from monai.config import print_config\n",
    "from monai.data import DataLoader, ImageDataset, CSVSaver\n",
    "from monai.transforms import (\n",
    "    EnsureChannelFirst,\n",
    "    Compose,\n",
    "    RandRotate90,\n",
    "    Resize,\n",
    "    Orientation,\n",
    "    ScaleIntensityRange\n",
    ")\n",
    "from monai.utils import set_determinism\n",
    "from PIL import Image\n",
    "import dicom2nifti.settings as settings\n",
    "import re\n",
    "\n",
    "pin_memory = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
    "print_config()\n",
    "\n",
    "# /kaggle/input/rsna-2023-abdominal-trauma-detection/"
   ]
  },
  {
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   "id": "edc83e0c",
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     "exception": false,
     "start_time": "2023-08-31T10:55:04.778664",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "set_determinism(seed=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a33ad45e",
   "metadata": {
    "papermill": {
     "duration": 0.01752,
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    "tags": []
   },
   "source": [
    "# <div style=\"color:white;display:inline-block;border-radius:5px;background-color:#FF7F50;font-family:Nexa;overflow:hidden\"><p style=\"padding:20px;color:white;overflow:hidden;font-size:80%;letter-spacing:0.5px;margin:0\"><b> </b> EDA on \"train.csv\"</p></div>\n",
    "\n",
    "#### Here i'm identifying patients having only one of the eight injuries. A few patients will be selected from each category and their series files will be converted to nifti files and used for training, testing and evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6af0c0e1",
   "metadata": {
    "execution": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(pd.read_csv('/kaggle/input/rsna-2023-abdominal-trauma-detection//train.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d32ac50b",
   "metadata": {
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     "duration": 0.018125,
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     "start_time": "2023-08-31T10:55:04.921406",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Columns names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1b841bd2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:04.977185Z",
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     "exception": false,
     "start_time": "2023-08-31T10:55:04.957554",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['patient_id', 'bowel_healthy', 'bowel_injury', 'extravasation_healthy',\n",
       "       'extravasation_injury', 'kidney_healthy', 'kidney_low', 'kidney_high',\n",
       "       'liver_healthy', 'liver_low', 'liver_high', 'spleen_healthy',\n",
       "       'spleen_low', 'spleen_high', 'any_injury'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8445a445",
   "metadata": {
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     "duration": 0.017815,
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     "exception": false,
     "start_time": "2023-08-31T10:55:05.003985",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Checking datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a851f59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:05.059089Z",
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     "start_time": "2023-08-31T10:55:05.039647",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patient_id               int64\n",
       "bowel_healthy            int64\n",
       "bowel_injury             int64\n",
       "extravasation_healthy    int64\n",
       "extravasation_injury     int64\n",
       "kidney_healthy           int64\n",
       "kidney_low               int64\n",
       "kidney_high              int64\n",
       "liver_healthy            int64\n",
       "liver_low                int64\n",
       "liver_high               int64\n",
       "spleen_healthy           int64\n",
       "spleen_low               int64\n",
       "spleen_high              int64\n",
       "any_injury               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70524336",
   "metadata": {
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     "duration": 0.017916,
     "end_time": "2023-08-31T10:55:05.106984",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.089068",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Changing datatype for all columns to boolean"
   ]
  },
  {
   "cell_type": "code",
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   "id": "871963b1",
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     "iopub.status.busy": "2023-08-31T10:55:05.144213Z",
     "iopub.status.idle": "2023-08-31T10:55:05.154244Z",
     "shell.execute_reply": "2023-08-31T10:55:05.153363Z"
    },
    "papermill": {
     "duration": 0.030914,
     "end_time": "2023-08-31T10:55:05.156236",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.125322",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for column in df.columns:\n",
    "    if column != 'patient_id':\n",
    "        df[column] = df[column].astype(bool)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55901474",
   "metadata": {
    "papermill": {
     "duration": 0.01802,
     "end_time": "2023-08-31T10:55:05.192325",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.174305",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Review changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "54d8acef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:05.230002Z",
     "iopub.status.busy": "2023-08-31T10:55:05.229208Z",
     "iopub.status.idle": "2023-08-31T10:55:05.236708Z",
     "shell.execute_reply": "2023-08-31T10:55:05.235712Z"
    },
    "papermill": {
     "duration": 0.028359,
     "end_time": "2023-08-31T10:55:05.238703",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.210344",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patient_id               int64\n",
       "bowel_healthy             bool\n",
       "bowel_injury              bool\n",
       "extravasation_healthy     bool\n",
       "extravasation_injury      bool\n",
       "kidney_healthy            bool\n",
       "kidney_low                bool\n",
       "kidney_high               bool\n",
       "liver_healthy             bool\n",
       "liver_low                 bool\n",
       "liver_high                bool\n",
       "spleen_healthy            bool\n",
       "spleen_low                bool\n",
       "spleen_high               bool\n",
       "any_injury                bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "526783cc",
   "metadata": {
    "papermill": {
     "duration": 0.017953,
     "end_time": "2023-08-31T10:55:05.274926",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.256973",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Dataframe shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aaa6d4d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:05.312998Z",
     "iopub.status.busy": "2023-08-31T10:55:05.312206Z",
     "iopub.status.idle": "2023-08-31T10:55:05.317870Z",
     "shell.execute_reply": "2023-08-31T10:55:05.316968Z"
    },
    "papermill": {
     "duration": 0.026678,
     "end_time": "2023-08-31T10:55:05.319815",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.293137",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns: 15\n",
      "Number of rows: 3147\n"
     ]
    }
   ],
   "source": [
    "# number of rows and columns\n",
    "number_of_columns = df.shape[1]\n",
    "number_of_rows = df.shape[0]\n",
    "print(f'Number of columns: {number_of_columns}')\n",
    "print(f'Number of rows: {number_of_rows}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e4d14c4",
   "metadata": {
    "papermill": {
     "duration": 0.018034,
     "end_time": "2023-08-31T10:55:05.356208",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.338174",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Patients with no injuries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c2e75440",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:05.394173Z",
     "iopub.status.busy": "2023-08-31T10:55:05.393368Z",
     "iopub.status.idle": "2023-08-31T10:55:05.424477Z",
     "shell.execute_reply": "2023-08-31T10:55:05.423396Z"
    },
    "papermill": {
     "duration": 0.052169,
     "end_time": "2023-08-31T10:55:05.426470",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.374301",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2292 such rows\n",
      "This means about 80% of the patients in 'train_images' have no injury.\n"
     ]
    },
    {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2292 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient_id  bowel_healthy  bowel_injury  extravasation_healthy  \\\n",
       "1          10005           True         False                   True   \n",
       "2          10007           True         False                   True   \n",
       "3          10026           True         False                   True   \n",
       "6          10082           True         False                   True   \n",
       "7          10104           True         False                   True   \n",
       "...          ...            ...           ...                    ...   \n",
       "3140        9835           True         False                   True   \n",
       "3141        9860           True         False                   True   \n",
       "3142        9951           True         False                   True   \n",
       "3143        9960           True         False                   True   \n",
       "3144        9961           True         False                   True   \n",
       "\n",
       "      extravasation_injury  kidney_healthy  kidney_low  kidney_high  \\\n",
       "1                    False            True       False        False   \n",
       "2                    False            True       False        False   \n",
       "3                    False            True       False        False   \n",
       "6                    False            True       False        False   \n",
       "7                    False            True       False        False   \n",
       "...                    ...             ...         ...          ...   \n",
       "3140                 False            True       False        False   \n",
       "3141                 False            True       False        False   \n",
       "3142                 False            True       False        False   \n",
       "3143                 False            True       False        False   \n",
       "3144                 False            True       False        False   \n",
       "\n",
       "      liver_healthy  liver_low  liver_high  spleen_healthy  spleen_low  \\\n",
       "1              True      False       False            True       False   \n",
       "2              True      False       False            True       False   \n",
       "3              True      False       False            True       False   \n",
       "6              True      False       False            True       False   \n",
       "7              True      False       False            True       False   \n",
       "...             ...        ...         ...             ...         ...   \n",
       "3140           True      False       False            True       False   \n",
       "3141           True      False       False            True       False   \n",
       "3142           True      False       False            True       False   \n",
       "3143           True      False       False            True       False   \n",
       "3144           True      False       False            True       False   \n",
       "\n",
       "      spleen_high  any_injury  \n",
       "1           False       False  \n",
       "2           False       False  \n",
       "3           False       False  \n",
       "6           False       False  \n",
       "7           False       False  \n",
       "...           ...         ...  \n",
       "3140        False       False  \n",
       "3141        False       False  \n",
       "3142        False       False  \n",
       "3143        False       False  \n",
       "3144        False       False  \n",
       "\n",
       "[2292 rows x 15 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "everything_healthy = df[(df['extravasation_healthy'] == True) & (df['bowel_healthy'] == True) & \n",
    "                                           (df['kidney_healthy'] == True) & (df['liver_healthy'] == True) &\n",
    "                                            (df['spleen_healthy'] == True)]\n",
    "print(f'There are {everything_healthy.shape[0]} such rows')\n",
    "print('This means about 80% of the patients in \\'train_images\\' have no injury.')\n",
    "everything_healthy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d216aa0",
   "metadata": {
    "papermill": {
     "duration": 0.02125,
     "end_time": "2023-08-31T10:55:05.466622",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.445372",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Patients with only a bowel injury"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aad40a1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:05.505947Z",
     "iopub.status.busy": "2023-08-31T10:55:05.505146Z",
     "iopub.status.idle": "2023-08-31T10:55:05.536306Z",
     "shell.execute_reply": "2023-08-31T10:55:05.535411Z"
    },
    "papermill": {
     "duration": 0.053147,
     "end_time": "2023-08-31T10:55:05.538526",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.485379",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 29 such rows\n"
     ]
    },
    {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>12332</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>12951</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13403</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>19763</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>19914</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>27196</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>29407</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1210</th>\n",
       "      <td>32011</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>32541</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1365</th>\n",
       "      <td>35022</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>35331</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1693</th>\n",
       "      <td>41050</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1792</th>\n",
       "      <td>43059</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>45303</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2325</th>\n",
       "      <td>53348</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2330</th>\n",
       "      <td>53395</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2334</th>\n",
       "      <td>53475</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2339</th>\n",
       "      <td>53581</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2395</th>\n",
       "      <td>54525</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2510</th>\n",
       "      <td>56441</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2540</th>\n",
       "      <td>56981</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2626</th>\n",
       "      <td>58465</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2771</th>\n",
       "      <td>61399</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2796</th>\n",
       "      <td>61834</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3031</th>\n",
       "      <td>7482</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3082</th>\n",
       "      <td>8684</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient_id  bowel_healthy  bowel_injury  extravasation_healthy  \\\n",
       "5          10065          False          True                   True   \n",
       "50         10929          False          True                   True   \n",
       "67         11335          False          True                   True   \n",
       "119        12332          False          True                   True   \n",
       "152        12951          False          True                   True   \n",
       "176        13403          False          True                   True   \n",
       "546        19763          False          True                   True   \n",
       "558        19914          False          True                   True   \n",
       "936        27196          False          True                   True   \n",
       "1057       29407          False          True                   True   \n",
       "1210       32011          False          True                   True   \n",
       "1238       32541          False          True                   True   \n",
       "1365       35022          False          True                   True   \n",
       "1384       35331          False          True                   True   \n",
       "1693       41050          False          True                   True   \n",
       "1792       43059          False          True                   True   \n",
       "1901       45303          False          True                   True   \n",
       "2325       53348          False          True                   True   \n",
       "2330       53395          False          True                   True   \n",
       "2334       53475          False          True                   True   \n",
       "2339       53581          False          True                   True   \n",
       "2395       54525          False          True                   True   \n",
       "2510       56441          False          True                   True   \n",
       "2540       56981          False          True                   True   \n",
       "2626       58465          False          True                   True   \n",
       "2771       61399          False          True                   True   \n",
       "2796       61834          False          True                   True   \n",
       "3031        7482          False          True                   True   \n",
       "3082        8684          False          True                   True   \n",
       "\n",
       "      extravasation_injury  kidney_healthy  kidney_low  kidney_high  \\\n",
       "5                    False            True       False        False   \n",
       "50                   False            True       False        False   \n",
       "67                   False            True       False        False   \n",
       "119                  False            True       False        False   \n",
       "152                  False            True       False        False   \n",
       "176                  False            True       False        False   \n",
       "546                  False            True       False        False   \n",
       "558                  False            True       False        False   \n",
       "936                  False            True       False        False   \n",
       "1057                 False            True       False        False   \n",
       "1210                 False            True       False        False   \n",
       "1238                 False            True       False        False   \n",
       "1365                 False            True       False        False   \n",
       "1384                 False            True       False        False   \n",
       "1693                 False            True       False        False   \n",
       "1792                 False            True       False        False   \n",
       "1901                 False            True       False        False   \n",
       "2325                 False            True       False        False   \n",
       "2330                 False            True       False        False   \n",
       "2334                 False            True       False        False   \n",
       "2339                 False            True       False        False   \n",
       "2395                 False            True       False        False   \n",
       "2510                 False            True       False        False   \n",
       "2540                 False            True       False        False   \n",
       "2626                 False            True       False        False   \n",
       "2771                 False            True       False        False   \n",
       "2796                 False            True       False        False   \n",
       "3031                 False            True       False        False   \n",
       "3082                 False            True       False        False   \n",
       "\n",
       "      liver_healthy  liver_low  liver_high  spleen_healthy  spleen_low  \\\n",
       "5              True      False       False            True       False   \n",
       "50             True      False       False            True       False   \n",
       "67             True      False       False            True       False   \n",
       "119            True      False       False            True       False   \n",
       "152            True      False       False            True       False   \n",
       "176            True      False       False            True       False   \n",
       "546            True      False       False            True       False   \n",
       "558            True      False       False            True       False   \n",
       "936            True      False       False            True       False   \n",
       "1057           True      False       False            True       False   \n",
       "1210           True      False       False            True       False   \n",
       "1238           True      False       False            True       False   \n",
       "1365           True      False       False            True       False   \n",
       "1384           True      False       False            True       False   \n",
       "1693           True      False       False            True       False   \n",
       "1792           True      False       False            True       False   \n",
       "1901           True      False       False            True       False   \n",
       "2325           True      False       False            True       False   \n",
       "2330           True      False       False            True       False   \n",
       "2334           True      False       False            True       False   \n",
       "2339           True      False       False            True       False   \n",
       "2395           True      False       False            True       False   \n",
       "2510           True      False       False            True       False   \n",
       "2540           True      False       False            True       False   \n",
       "2626           True      False       False            True       False   \n",
       "2771           True      False       False            True       False   \n",
       "2796           True      False       False            True       False   \n",
       "3031           True      False       False            True       False   \n",
       "3082           True      False       False            True       False   \n",
       "\n",
       "      spleen_high  any_injury  \n",
       "5           False        True  \n",
       "50          False        True  \n",
       "67          False        True  \n",
       "119         False        True  \n",
       "152         False        True  \n",
       "176         False        True  \n",
       "546         False        True  \n",
       "558         False        True  \n",
       "936         False        True  \n",
       "1057        False        True  \n",
       "1210        False        True  \n",
       "1238        False        True  \n",
       "1365        False        True  \n",
       "1384        False        True  \n",
       "1693        False        True  \n",
       "1792        False        True  \n",
       "1901        False        True  \n",
       "2325        False        True  \n",
       "2330        False        True  \n",
       "2334        False        True  \n",
       "2339        False        True  \n",
       "2395        False        True  \n",
       "2510        False        True  \n",
       "2540        False        True  \n",
       "2626        False        True  \n",
       "2771        False        True  \n",
       "2796        False        True  \n",
       "3031        False        True  \n",
       "3082        False        True  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bowel_injury_only  = df[(df['extravasation_healthy'] == True) & (df['bowel_injury'] == True) & \n",
    "                                           (df['kidney_healthy'] == True) & (df['liver_healthy'] == True) &\n",
    "                                            (df['spleen_healthy'] == True)]\n",
    "print(f'There are {bowel_injury_only.shape[0]} such rows')                                           \n",
    "bowel_injury_only  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0218584b",
   "metadata": {
    "papermill": {
     "duration": 0.020135,
     "end_time": "2023-08-31T10:55:05.578823",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.558688",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Patients with only an extravasation injury"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "893fe9d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:05.619794Z",
     "iopub.status.busy": "2023-08-31T10:55:05.619448Z",
     "iopub.status.idle": "2023-08-31T10:55:05.644184Z",
     "shell.execute_reply": "2023-08-31T10:55:05.643191Z"
    },
    "papermill": {
     "duration": 0.047758,
     "end_time": "2023-08-31T10:55:05.646218",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.598460",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 97 such rows\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>bowel_healthy</th>\n",
       "      <th>bowel_injury</th>\n",
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       "      <th>extravasation_injury</th>\n",
       "      <th>kidney_healthy</th>\n",
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       "      <th>kidney_high</th>\n",
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       "      <th>liver_low</th>\n",
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       "      <th>22</th>\n",
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       "      <th>28</th>\n",
       "      <td>10494</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "      <th>57</th>\n",
       "      <td>11044</td>\n",
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       "      <td>True</td>\n",
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       "      <th>70</th>\n",
       "      <td>11379</td>\n",
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       "      <td>11474</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3132</th>\n",
       "      <td>9632</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient_id  bowel_healthy  bowel_injury  extravasation_healthy  \\\n",
       "22         10292           True         False                  False   \n",
       "28         10494           True         False                  False   \n",
       "57         11044           True         False                  False   \n",
       "70         11379           True         False                  False   \n",
       "76         11474           True         False                  False   \n",
       "...          ...            ...           ...                    ...   \n",
       "2999        7026           True         False                  False   \n",
       "3025        7411           True         False                  False   \n",
       "3051        7944           True         False                  False   \n",
       "3122        9528           True         False                  False   \n",
       "3132        9632           True         False                  False   \n",
       "\n",
       "      extravasation_injury  kidney_healthy  kidney_low  kidney_high  \\\n",
       "22                    True            True       False        False   \n",
       "28                    True            True       False        False   \n",
       "57                    True            True       False        False   \n",
       "70                    True            True       False        False   \n",
       "76                    True            True       False        False   \n",
       "...                    ...             ...         ...          ...   \n",
       "2999                  True            True       False        False   \n",
       "3025                  True            True       False        False   \n",
       "3051                  True            True       False        False   \n",
       "3122                  True            True       False        False   \n",
       "3132                  True            True       False        False   \n",
       "\n",
       "      liver_healthy  liver_low  liver_high  spleen_healthy  spleen_low  \\\n",
       "22             True      False       False            True       False   \n",
       "28             True      False       False            True       False   \n",
       "57             True      False       False            True       False   \n",
       "70             True      False       False            True       False   \n",
       "76             True      False       False            True       False   \n",
       "...             ...        ...         ...             ...         ...   \n",
       "2999           True      False       False            True       False   \n",
       "3025           True      False       False            True       False   \n",
       "3051           True      False       False            True       False   \n",
       "3122           True      False       False            True       False   \n",
       "3132           True      False       False            True       False   \n",
       "\n",
       "      spleen_high  any_injury  \n",
       "22          False        True  \n",
       "28          False        True  \n",
       "57          False        True  \n",
       "70          False        True  \n",
       "76          False        True  \n",
       "...           ...         ...  \n",
       "2999        False        True  \n",
       "3025        False        True  \n",
       "3051        False        True  \n",
       "3122        False        True  \n",
       "3132        False        True  \n",
       "\n",
       "[97 rows x 15 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extravasation_injury_only = df[(df['extravasation_injury'] == True) & (df['bowel_healthy'] == True) & \n",
    "                                           (df['kidney_healthy'] == True) & (df['liver_healthy'] == True) &\n",
    "                                            (df['spleen_healthy'] == True)]\n",
    "print(f'There are {extravasation_injury_only.shape[0]} such rows')                                             \n",
    "extravasation_injury_only"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66870908",
   "metadata": {
    "papermill": {
     "duration": 0.02004,
     "end_time": "2023-08-31T10:55:05.686965",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.666925",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Patients with only a low grade kidney injury"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3c961345",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:05.729492Z",
     "iopub.status.busy": "2023-08-31T10:55:05.728572Z",
     "iopub.status.idle": "2023-08-31T10:55:05.752203Z",
     "shell.execute_reply": "2023-08-31T10:55:05.751169Z"
    },
    "papermill": {
     "duration": 0.046954,
     "end_time": "2023-08-31T10:55:05.754279",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.707325",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 64 such rows\n"
     ]
    },
    {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>2851</th>\n",
       "      <td>63226</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3040</th>\n",
       "      <td>7657</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3045</th>\n",
       "      <td>7754</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3115</th>\n",
       "      <td>9424</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3125</th>\n",
       "      <td>9549</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient_id  bowel_healthy  bowel_injury  extravasation_healthy  \\\n",
       "63         11222           True         False                   True   \n",
       "69         11378           True         False                   True   \n",
       "201         1381           True         False                   True   \n",
       "218        14281           True         False                   True   \n",
       "267        15057           True         False                   True   \n",
       "...          ...            ...           ...                    ...   \n",
       "2851       63226           True         False                   True   \n",
       "3040        7657           True         False                   True   \n",
       "3045        7754           True         False                   True   \n",
       "3115        9424           True         False                   True   \n",
       "3125        9549           True         False                   True   \n",
       "\n",
       "      extravasation_injury  kidney_healthy  kidney_low  kidney_high  \\\n",
       "63                   False           False        True        False   \n",
       "69                   False           False        True        False   \n",
       "201                  False           False        True        False   \n",
       "218                  False           False        True        False   \n",
       "267                  False           False        True        False   \n",
       "...                    ...             ...         ...          ...   \n",
       "2851                 False           False        True        False   \n",
       "3040                 False           False        True        False   \n",
       "3045                 False           False        True        False   \n",
       "3115                 False           False        True        False   \n",
       "3125                 False           False        True        False   \n",
       "\n",
       "      liver_healthy  liver_low  liver_high  spleen_healthy  spleen_low  \\\n",
       "63             True      False       False            True       False   \n",
       "69             True      False       False            True       False   \n",
       "201            True      False       False            True       False   \n",
       "218            True      False       False            True       False   \n",
       "267            True      False       False            True       False   \n",
       "...             ...        ...         ...             ...         ...   \n",
       "2851           True      False       False            True       False   \n",
       "3040           True      False       False            True       False   \n",
       "3045           True      False       False            True       False   \n",
       "3115           True      False       False            True       False   \n",
       "3125           True      False       False            True       False   \n",
       "\n",
       "      spleen_high  any_injury  \n",
       "63          False        True  \n",
       "69          False        True  \n",
       "201         False        True  \n",
       "218         False        True  \n",
       "267         False        True  \n",
       "...           ...         ...  \n",
       "2851        False        True  \n",
       "3040        False        True  \n",
       "3045        False        True  \n",
       "3115        False        True  \n",
       "3125        False        True  \n",
       "\n",
       "[64 rows x 15 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kidney_low_only = df[(df['extravasation_healthy'] == True) & (df['bowel_healthy'] == True) & \n",
    "                                           (df['kidney_low'] == True) & (df['liver_healthy'] == True) &\n",
    "                                            (df['spleen_healthy'] == True)]\n",
    "print(f'There are {kidney_low_only.shape[0]} such rows')                                            \n",
    "kidney_low_only "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88bef0db",
   "metadata": {
    "papermill": {
     "duration": 0.020595,
     "end_time": "2023-08-31T10:55:05.796145",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.775550",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Patients with only a high grade kidney injury"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e51cd11a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:05.840125Z",
     "iopub.status.busy": "2023-08-31T10:55:05.839786Z",
     "iopub.status.idle": "2023-08-31T10:55:05.866286Z",
     "shell.execute_reply": "2023-08-31T10:55:05.865377Z"
    },
    "papermill": {
     "duration": 0.051326,
     "end_time": "2023-08-31T10:55:05.868578",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.817252",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 18 such rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>bowel_healthy</th>\n",
       "      <th>bowel_injury</th>\n",
       "      <th>extravasation_healthy</th>\n",
       "      <th>extravasation_injury</th>\n",
       "      <th>kidney_healthy</th>\n",
       "      <th>kidney_low</th>\n",
       "      <th>kidney_high</th>\n",
       "      <th>liver_healthy</th>\n",
       "      <th>liver_low</th>\n",
       "      <th>liver_high</th>\n",
       "      <th>spleen_healthy</th>\n",
       "      <th>spleen_low</th>\n",
       "      <th>spleen_high</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>1452</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>19050</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1064</th>\n",
       "      <td>29511</td>\n",
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       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>30613</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1375</th>\n",
       "      <td>35168</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1411</th>\n",
       "      <td>35794</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1413</th>\n",
       "      <td>3580</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1661</th>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>40912</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2062</th>\n",
       "      <td>48540</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2090</th>\n",
       "      <td>49044</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2200</th>\n",
       "      <td>50925</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2207</th>\n",
       "      <td>51039</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2370</th>\n",
       "      <td>54190</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2597</th>\n",
       "      <td>57981</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2754</th>\n",
       "      <td>60934</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2760</th>\n",
       "      <td>61005</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient_id  bowel_healthy  bowel_injury  extravasation_healthy  \\\n",
       "161        13098           True         False                   True   \n",
       "234         1452           True         False                   True   \n",
       "499        19050           True         False                   True   \n",
       "1064       29511           True         False                   True   \n",
       "1127       30613           True         False                   True   \n",
       "1375       35168           True         False                   True   \n",
       "1411       35794           True         False                   True   \n",
       "1413        3580           True         False                   True   \n",
       "1661       40473           True         False                   True   \n",
       "1685       40912           True         False                   True   \n",
       "2062       48540           True         False                   True   \n",
       "2090       49044           True         False                   True   \n",
       "2200       50925           True         False                   True   \n",
       "2207       51039           True         False                   True   \n",
       "2370       54190           True         False                   True   \n",
       "2597       57981           True         False                   True   \n",
       "2754       60934           True         False                   True   \n",
       "2760       61005           True         False                   True   \n",
       "\n",
       "      extravasation_injury  kidney_healthy  kidney_low  kidney_high  \\\n",
       "161                  False           False       False         True   \n",
       "234                  False           False       False         True   \n",
       "499                  False           False       False         True   \n",
       "1064                 False           False       False         True   \n",
       "1127                 False           False       False         True   \n",
       "1375                 False           False       False         True   \n",
       "1411                 False           False       False         True   \n",
       "1413                 False           False       False         True   \n",
       "1661                 False           False       False         True   \n",
       "1685                 False           False       False         True   \n",
       "2062                 False           False       False         True   \n",
       "2090                 False           False       False         True   \n",
       "2200                 False           False       False         True   \n",
       "2207                 False           False       False         True   \n",
       "2370                 False           False       False         True   \n",
       "2597                 False           False       False         True   \n",
       "2754                 False           False       False         True   \n",
       "2760                 False           False       False         True   \n",
       "\n",
       "      liver_healthy  liver_low  liver_high  spleen_healthy  spleen_low  \\\n",
       "161            True      False       False            True       False   \n",
       "234            True      False       False            True       False   \n",
       "499            True      False       False            True       False   \n",
       "1064           True      False       False            True       False   \n",
       "1127           True      False       False            True       False   \n",
       "1375           True      False       False            True       False   \n",
       "1411           True      False       False            True       False   \n",
       "1413           True      False       False            True       False   \n",
       "1661           True      False       False            True       False   \n",
       "1685           True      False       False            True       False   \n",
       "2062           True      False       False            True       False   \n",
       "2090           True      False       False            True       False   \n",
       "2200           True      False       False            True       False   \n",
       "2207           True      False       False            True       False   \n",
       "2370           True      False       False            True       False   \n",
       "2597           True      False       False            True       False   \n",
       "2754           True      False       False            True       False   \n",
       "2760           True      False       False            True       False   \n",
       "\n",
       "      spleen_high  any_injury  \n",
       "161         False        True  \n",
       "234         False        True  \n",
       "499         False        True  \n",
       "1064        False        True  \n",
       "1127        False        True  \n",
       "1375        False        True  \n",
       "1411        False        True  \n",
       "1413        False        True  \n",
       "1661        False        True  \n",
       "1685        False        True  \n",
       "2062        False        True  \n",
       "2090        False        True  \n",
       "2200        False        True  \n",
       "2207        False        True  \n",
       "2370        False        True  \n",
       "2597        False        True  \n",
       "2754        False        True  \n",
       "2760        False        True  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kidney_high_only = df[(df['extravasation_healthy'] == True) & (df['bowel_healthy'] == True) & \n",
    "                                           (df['kidney_high'] == True) & (df['liver_healthy'] == True) &\n",
    "                                            (df['spleen_healthy'] == True)]\n",
    "print(f'There are {kidney_high_only.shape[0]} such rows')                                            \n",
    "kidney_high_only "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61ce87b2",
   "metadata": {
    "papermill": {
     "duration": 0.021521,
     "end_time": "2023-08-31T10:55:05.911571",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.890050",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Patients with only a low grade spleen injury"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "eef661c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:05.956163Z",
     "iopub.status.busy": "2023-08-31T10:55:05.955851Z",
     "iopub.status.idle": "2023-08-31T10:55:05.981077Z",
     "shell.execute_reply": "2023-08-31T10:55:05.980172Z"
    },
    "papermill": {
     "duration": 0.049681,
     "end_time": "2023-08-31T10:55:05.983112",
     "exception": false,
     "start_time": "2023-08-31T10:55:05.933431",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 146 such rows\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>bowel_healthy</th>\n",
       "      <th>bowel_injury</th>\n",
       "      <th>extravasation_healthy</th>\n",
       "      <th>extravasation_injury</th>\n",
       "      <th>kidney_healthy</th>\n",
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       "      <th>liver_healthy</th>\n",
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       "      <th>4</th>\n",
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       "      <th>10</th>\n",
       "      <td>10127</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>11021</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3112</th>\n",
       "      <td>9386</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient_id  bowel_healthy  bowel_injury  extravasation_healthy  \\\n",
       "4          10051           True         False                   True   \n",
       "10         10127           True         False                   True   \n",
       "36          1060           True         False                   True   \n",
       "37         10683           True         False                   True   \n",
       "55         11021           True         False                   True   \n",
       "...          ...            ...           ...                    ...   \n",
       "3058        8094           True         False                   True   \n",
       "3095        8964           True         False                   True   \n",
       "3096        8978           True         False                   True   \n",
       "3098         902           True         False                   True   \n",
       "3112        9386           True         False                   True   \n",
       "\n",
       "      extravasation_injury  kidney_healthy  kidney_low  kidney_high  \\\n",
       "4                    False            True       False        False   \n",
       "10                   False            True       False        False   \n",
       "36                   False            True       False        False   \n",
       "37                   False            True       False        False   \n",
       "55                   False            True       False        False   \n",
       "...                    ...             ...         ...          ...   \n",
       "3058                 False            True       False        False   \n",
       "3095                 False            True       False        False   \n",
       "3096                 False            True       False        False   \n",
       "3098                 False            True       False        False   \n",
       "3112                 False            True       False        False   \n",
       "\n",
       "      liver_healthy  liver_low  liver_high  spleen_healthy  spleen_low  \\\n",
       "4              True      False       False           False        True   \n",
       "10             True      False       False           False        True   \n",
       "36             True      False       False           False        True   \n",
       "37             True      False       False           False        True   \n",
       "55             True      False       False           False        True   \n",
       "...             ...        ...         ...             ...         ...   \n",
       "3058           True      False       False           False        True   \n",
       "3095           True      False       False           False        True   \n",
       "3096           True      False       False           False        True   \n",
       "3098           True      False       False           False        True   \n",
       "3112           True      False       False           False        True   \n",
       "\n",
       "      spleen_high  any_injury  \n",
       "4           False        True  \n",
       "10          False        True  \n",
       "36          False        True  \n",
       "37          False        True  \n",
       "55          False        True  \n",
       "...           ...         ...  \n",
       "3058        False        True  \n",
       "3095        False        True  \n",
       "3096        False        True  \n",
       "3098        False        True  \n",
       "3112        False        True  \n",
       "\n",
       "[146 rows x 15 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spleen_low_only = df[(df['extravasation_healthy'] == True) & (df['bowel_healthy'] == True) & \n",
    "                                           (df['kidney_healthy'] == True) & (df['liver_healthy'] == True) &\n",
    "                                            (df['spleen_low'] == True)]\n",
    "print(f'There are {spleen_low_only.shape[0]} such rows')  \n",
    "spleen_low_only"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dc023c5",
   "metadata": {
    "papermill": {
     "duration": 0.021775,
     "end_time": "2023-08-31T10:55:06.026958",
     "exception": false,
     "start_time": "2023-08-31T10:55:06.005183",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Patients with only a high grade spleen injury"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5def9903",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:06.072228Z",
     "iopub.status.busy": "2023-08-31T10:55:06.071650Z",
     "iopub.status.idle": "2023-08-31T10:55:06.095451Z",
     "shell.execute_reply": "2023-08-31T10:55:06.094482Z"
    },
    "papermill": {
     "duration": 0.048773,
     "end_time": "2023-08-31T10:55:06.097550",
     "exception": false,
     "start_time": "2023-08-31T10:55:06.048777",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 81 such rows\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>14784</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2928</th>\n",
       "      <td>64632</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>6990</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3022</th>\n",
       "      <td>7369</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3145</th>\n",
       "      <td>9980</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3146</th>\n",
       "      <td>9983</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient_id  bowel_healthy  bowel_injury  extravasation_healthy  \\\n",
       "11         10132           True         False                   True   \n",
       "23         10295           True         False                   True   \n",
       "29         10503           True         False                   True   \n",
       "49         10925           True         False                   True   \n",
       "249        14784           True         False                   True   \n",
       "...          ...            ...           ...                    ...   \n",
       "2928       64632           True         False                   True   \n",
       "2995        6990           True         False                   True   \n",
       "3022        7369           True         False                   True   \n",
       "3145        9980           True         False                   True   \n",
       "3146        9983           True         False                   True   \n",
       "\n",
       "      extravasation_injury  kidney_healthy  kidney_low  kidney_high  \\\n",
       "11                   False            True       False        False   \n",
       "23                   False            True       False        False   \n",
       "29                   False            True       False        False   \n",
       "49                   False            True       False        False   \n",
       "249                  False            True       False        False   \n",
       "...                    ...             ...         ...          ...   \n",
       "2928                 False            True       False        False   \n",
       "2995                 False            True       False        False   \n",
       "3022                 False            True       False        False   \n",
       "3145                 False            True       False        False   \n",
       "3146                 False            True       False        False   \n",
       "\n",
       "      liver_healthy  liver_low  liver_high  spleen_healthy  spleen_low  \\\n",
       "11             True      False       False           False       False   \n",
       "23             True      False       False           False       False   \n",
       "29             True      False       False           False       False   \n",
       "49             True      False       False           False       False   \n",
       "249            True      False       False           False       False   \n",
       "...             ...        ...         ...             ...         ...   \n",
       "2928           True      False       False           False       False   \n",
       "2995           True      False       False           False       False   \n",
       "3022           True      False       False           False       False   \n",
       "3145           True      False       False           False       False   \n",
       "3146           True      False       False           False       False   \n",
       "\n",
       "      spleen_high  any_injury  \n",
       "11           True        True  \n",
       "23           True        True  \n",
       "29           True        True  \n",
       "49           True        True  \n",
       "249          True        True  \n",
       "...           ...         ...  \n",
       "2928         True        True  \n",
       "2995         True        True  \n",
       "3022         True        True  \n",
       "3145         True        True  \n",
       "3146         True        True  \n",
       "\n",
       "[81 rows x 15 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spleen_high_only = df[(df['extravasation_healthy'] == True) & (df['bowel_healthy'] == True) & \n",
    "                                           (df['kidney_healthy'] == True) & (df['liver_healthy'] == True) &\n",
    "                                            (df['spleen_high'] == True)]\n",
    "print(f'There are {spleen_high_only.shape[0]} such rows')                                               \n",
    "spleen_high_only  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22678c56",
   "metadata": {
    "papermill": {
     "duration": 0.02272,
     "end_time": "2023-08-31T10:55:06.142993",
     "exception": false,
     "start_time": "2023-08-31T10:55:06.120273",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Patients with only a low grade liver injury"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "07ec2f6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:06.190196Z",
     "iopub.status.busy": "2023-08-31T10:55:06.189371Z",
     "iopub.status.idle": "2023-08-31T10:55:06.214009Z",
     "shell.execute_reply": "2023-08-31T10:55:06.213103Z"
    },
    "papermill": {
     "duration": 0.050612,
     "end_time": "2023-08-31T10:55:06.216042",
     "exception": false,
     "start_time": "2023-08-31T10:55:06.165430",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 178 such rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>bowel_healthy</th>\n",
       "      <th>bowel_injury</th>\n",
       "      <th>extravasation_healthy</th>\n",
       "      <th>extravasation_injury</th>\n",
       "      <th>kidney_healthy</th>\n",
       "      <th>kidney_low</th>\n",
       "      <th>kidney_high</th>\n",
       "      <th>liver_healthy</th>\n",
       "      <th>liver_low</th>\n",
       "      <th>liver_high</th>\n",
       "      <th>spleen_healthy</th>\n",
       "      <th>spleen_low</th>\n",
       "      <th>spleen_high</th>\n",
       "      <th>any_injury</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10430</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>12198</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>121</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>12451</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3048</th>\n",
       "      <td>7819</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3056</th>\n",
       "      <td>8054</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3080</th>\n",
       "      <td>8621</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3101</th>\n",
       "      <td>9081</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3103</th>\n",
       "      <td>9125</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient_id  bowel_healthy  bowel_injury  extravasation_healthy  \\\n",
       "12         10163           True         False                   True   \n",
       "25         10430           True         False                   True   \n",
       "111        12198           True         False                   True   \n",
       "121        12354           True         False                   True   \n",
       "126        12451           True         False                   True   \n",
       "...          ...            ...           ...                    ...   \n",
       "3048        7819           True         False                   True   \n",
       "3056        8054           True         False                   True   \n",
       "3080        8621           True         False                   True   \n",
       "3101        9081           True         False                   True   \n",
       "3103        9125           True         False                   True   \n",
       "\n",
       "      extravasation_injury  kidney_healthy  kidney_low  kidney_high  \\\n",
       "12                   False            True       False        False   \n",
       "25                   False            True       False        False   \n",
       "111                  False            True       False        False   \n",
       "121                  False            True       False        False   \n",
       "126                  False            True       False        False   \n",
       "...                    ...             ...         ...          ...   \n",
       "3048                 False            True       False        False   \n",
       "3056                 False            True       False        False   \n",
       "3080                 False            True       False        False   \n",
       "3101                 False            True       False        False   \n",
       "3103                 False            True       False        False   \n",
       "\n",
       "      liver_healthy  liver_low  liver_high  spleen_healthy  spleen_low  \\\n",
       "12            False       True       False            True       False   \n",
       "25            False       True       False            True       False   \n",
       "111           False       True       False            True       False   \n",
       "121           False       True       False            True       False   \n",
       "126           False       True       False            True       False   \n",
       "...             ...        ...         ...             ...         ...   \n",
       "3048          False       True       False            True       False   \n",
       "3056          False       True       False            True       False   \n",
       "3080          False       True       False            True       False   \n",
       "3101          False       True       False            True       False   \n",
       "3103          False       True       False            True       False   \n",
       "\n",
       "      spleen_high  any_injury  \n",
       "12          False        True  \n",
       "25          False        True  \n",
       "111         False        True  \n",
       "121         False        True  \n",
       "126         False        True  \n",
       "...           ...         ...  \n",
       "3048        False        True  \n",
       "3056        False        True  \n",
       "3080        False        True  \n",
       "3101        False        True  \n",
       "3103        False        True  \n",
       "\n",
       "[178 rows x 15 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_low_only = df[(df['extravasation_healthy'] == True) & (df['bowel_healthy'] == True) & \n",
    "                                           (df['kidney_healthy'] == True) & (df['liver_low'] == True) &\n",
    "                                            (df['spleen_healthy'] == True)]\n",
    "print(f'There are {liver_low_only.shape[0]} such rows')                                              \n",
    "liver_low_only "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bebce91",
   "metadata": {
    "papermill": {
     "duration": 0.023391,
     "end_time": "2023-08-31T10:55:06.263009",
     "exception": false,
     "start_time": "2023-08-31T10:55:06.239618",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Patients with only a high grade liver injury"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bcfa810d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:06.310781Z",
     "iopub.status.busy": "2023-08-31T10:55:06.309872Z",
     "iopub.status.idle": "2023-08-31T10:55:06.341587Z",
     "shell.execute_reply": "2023-08-31T10:55:06.340732Z"
    },
    "papermill": {
     "duration": 0.057836,
     "end_time": "2023-08-31T10:55:06.343771",
     "exception": false,
     "start_time": "2023-08-31T10:55:06.285935",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 31 such rows\n"
     ]
    },
    {
     "data": {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <th>1668</th>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
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       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2143</th>\n",
       "      <td>49853</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2435</th>\n",
       "      <td>55240</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2599</th>\n",
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       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient_id  bowel_healthy  bowel_injury  extravasation_healthy  \\\n",
       "120        12347           True         False                   True   \n",
       "206        13921           True         False                   True   \n",
       "372        16776           True         False                   True   \n",
       "392        17190           True         False                   True   \n",
       "422        17806           True         False                   True   \n",
       "461        18407           True         False                   True   \n",
       "494         1900           True         False                   True   \n",
       "534         1960           True         False                   True   \n",
       "564        20118           True         False                   True   \n",
       "566        20135           True         False                   True   \n",
       "598        21050           True         False                   True   \n",
       "668        22323           True         False                   True   \n",
       "1053        2937           True         False                   True   \n",
       "1079       29692           True         False                   True   \n",
       "1337       34401           True         False                   True   \n",
       "1402        3568           True         False                   True   \n",
       "1456       36576           True         False                   True   \n",
       "1491       37119           True         False                   True   \n",
       "1668       40633           True         False                   True   \n",
       "1703       41252           True         False                   True   \n",
       "1713       41450           True         False                   True   \n",
       "1771       42742           True         False                   True   \n",
       "2037        4813           True         False                   True   \n",
       "2143       49853           True         False                   True   \n",
       "2435       55240           True         False                   True   \n",
       "2599       57988           True         False                   True   \n",
       "2678       59302           True         False                   True   \n",
       "2730       60450           True         False                   True   \n",
       "2804       62022           True         False                   True   \n",
       "2819        6230           True         False                   True   \n",
       "3119        9493           True         False                   True   \n",
       "\n",
       "      extravasation_injury  kidney_healthy  kidney_low  kidney_high  \\\n",
       "120                  False            True       False        False   \n",
       "206                  False            True       False        False   \n",
       "372                  False            True       False        False   \n",
       "392                  False            True       False        False   \n",
       "422                  False            True       False        False   \n",
       "461                  False            True       False        False   \n",
       "494                  False            True       False        False   \n",
       "534                  False            True       False        False   \n",
       "564                  False            True       False        False   \n",
       "566                  False            True       False        False   \n",
       "598                  False            True       False        False   \n",
       "668                  False            True       False        False   \n",
       "1053                 False            True       False        False   \n",
       "1079                 False            True       False        False   \n",
       "1337                 False            True       False        False   \n",
       "1402                 False            True       False        False   \n",
       "1456                 False            True       False        False   \n",
       "1491                 False            True       False        False   \n",
       "1668                 False            True       False        False   \n",
       "1703                 False            True       False        False   \n",
       "1713                 False            True       False        False   \n",
       "1771                 False            True       False        False   \n",
       "2037                 False            True       False        False   \n",
       "2143                 False            True       False        False   \n",
       "2435                 False            True       False        False   \n",
       "2599                 False            True       False        False   \n",
       "2678                 False            True       False        False   \n",
       "2730                 False            True       False        False   \n",
       "2804                 False            True       False        False   \n",
       "2819                 False            True       False        False   \n",
       "3119                 False            True       False        False   \n",
       "\n",
       "      liver_healthy  liver_low  liver_high  spleen_healthy  spleen_low  \\\n",
       "120           False      False        True            True       False   \n",
       "206           False      False        True            True       False   \n",
       "372           False      False        True            True       False   \n",
       "392           False      False        True            True       False   \n",
       "422           False      False        True            True       False   \n",
       "461           False      False        True            True       False   \n",
       "494           False      False        True            True       False   \n",
       "534           False      False        True            True       False   \n",
       "564           False      False        True            True       False   \n",
       "566           False      False        True            True       False   \n",
       "598           False      False        True            True       False   \n",
       "668           False      False        True            True       False   \n",
       "1053          False      False        True            True       False   \n",
       "1079          False      False        True            True       False   \n",
       "1337          False      False        True            True       False   \n",
       "1402          False      False        True            True       False   \n",
       "1456          False      False        True            True       False   \n",
       "1491          False      False        True            True       False   \n",
       "1668          False      False        True            True       False   \n",
       "1703          False      False        True            True       False   \n",
       "1713          False      False        True            True       False   \n",
       "1771          False      False        True            True       False   \n",
       "2037          False      False        True            True       False   \n",
       "2143          False      False        True            True       False   \n",
       "2435          False      False        True            True       False   \n",
       "2599          False      False        True            True       False   \n",
       "2678          False      False        True            True       False   \n",
       "2730          False      False        True            True       False   \n",
       "2804          False      False        True            True       False   \n",
       "2819          False      False        True            True       False   \n",
       "3119          False      False        True            True       False   \n",
       "\n",
       "      spleen_high  any_injury  \n",
       "120         False        True  \n",
       "206         False        True  \n",
       "372         False        True  \n",
       "392         False        True  \n",
       "422         False        True  \n",
       "461         False        True  \n",
       "494         False        True  \n",
       "534         False        True  \n",
       "564         False        True  \n",
       "566         False        True  \n",
       "598         False        True  \n",
       "668         False        True  \n",
       "1053        False        True  \n",
       "1079        False        True  \n",
       "1337        False        True  \n",
       "1402        False        True  \n",
       "1456        False        True  \n",
       "1491        False        True  \n",
       "1668        False        True  \n",
       "1703        False        True  \n",
       "1713        False        True  \n",
       "1771        False        True  \n",
       "2037        False        True  \n",
       "2143        False        True  \n",
       "2435        False        True  \n",
       "2599        False        True  \n",
       "2678        False        True  \n",
       "2730        False        True  \n",
       "2804        False        True  \n",
       "2819        False        True  \n",
       "3119        False        True  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_high_only = df[(df['extravasation_healthy'] == True) & (df['bowel_healthy'] == True) & \n",
    "                                           (df['kidney_healthy'] == True) & (df['liver_high'] == True) &\n",
    "                                            (df['spleen_healthy'] == True)]\n",
    "print(f'There are {liver_high_only.shape[0]} such rows')                                              \n",
    "liver_high_only "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bc3a733",
   "metadata": {
    "papermill": {
     "duration": 0.023761,
     "end_time": "2023-08-31T10:55:06.391840",
     "exception": false,
     "start_time": "2023-08-31T10:55:06.368079",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <div style=\"color:white;display:inline-block;border-radius:5px;background-color:#FF7F50;font-family:Nexa;overflow:hidden\"><p style=\"padding:20px;color:white;overflow:hidden;font-size:80%;letter-spacing:0.5px;margin:0\"><b> </b> Dataset preparation and preprocessing</p></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0b7cf6bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:06.441358Z",
     "iopub.status.busy": "2023-08-31T10:55:06.440542Z",
     "iopub.status.idle": "2023-08-31T10:55:06.447238Z",
     "shell.execute_reply": "2023-08-31T10:55:06.446372Z"
    },
    "papermill": {
     "duration": 0.033344,
     "end_time": "2023-08-31T10:55:06.449263",
     "exception": false,
     "start_time": "2023-08-31T10:55:06.415919",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "root_dir =  '/kaggle/input/rsna-2023-abdominal-trauma-detection'\n",
    "working_dir = '/kaggle/working'\n",
    "# method to help me calculate the number of dcm images for each scan.\n",
    "def count_dcm(patient_id):\n",
    "    nested_folders = os.listdir(os.path.join(root_dir, f\"train_images/{patient_id}\"))\n",
    "    \n",
    "    for nested_folder in nested_folders:\n",
    "        path_to_nested_folder = os.path.join(root_dir, f\"train_images/{patient_id}\", nested_folder)\n",
    "        number_of_dcm_images = int(len(glob(path_to_nested_folder + '/*')))\n",
    "        print(f'Series number: {nested_folder}, Number of dcm images: {number_of_dcm_images}')\n",
    "        \n",
    "# method to help me cleanup a specified directory.\n",
    "def wipe_directory(directory_path):\n",
    "    shutil.rmtree(directory_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa403373",
   "metadata": {
    "papermill": {
     "duration": 0.023622,
     "end_time": "2023-08-31T10:55:06.499597",
     "exception": false,
     "start_time": "2023-08-31T10:55:06.475975",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Specifying which patient files were selected for each injury"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ad9fa96d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:06.549171Z",
     "iopub.status.busy": "2023-08-31T10:55:06.548430Z",
     "iopub.status.idle": "2023-08-31T10:55:06.559923Z",
     "shell.execute_reply": "2023-08-31T10:55:06.559041Z"
    },
    "papermill": {
     "duration": 0.038582,
     "end_time": "2023-08-31T10:55:06.561893",
     "exception": false,
     "start_time": "2023-08-31T10:55:06.523311",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# list_of_training_images = glob(os.path.join(root_dir, \"train_images/*\"))\n",
    "\n",
    "bowel_injury_files = ['10065', '10929', '11335', '35022', '45303', '13403', '19763',\n",
    "                     '32541', '56441', '58465', '53348', '41050', '29407', '27196',\n",
    "                     '53581', '7482', '56981', '61399', '32011', '43059', '12951',\n",
    "                     '19914', '35331', '12332']\n",
    "\n",
    "extravasation_injury_files = ['10292', '10494', '11044', '11379', '11474', '7026',\n",
    "                             '7411', '7944', '9528', '9632', '12210', '12875', '23029',\n",
    "                             '2602', '21325', '2209', '12192', '15188', '1675', '18779',\n",
    "                             '4852', '56082', '51476', '5337', '49096', '50021']\n",
    "\n",
    "kidney_injury_files = ['13098', '1452','19050', '29511', '30613', '35168', '35794', '3580',\n",
    "                       '40473', '40912', '48540', '49044', '61005', '54190', '60934', '57981',\n",
    "                        '11222', '11378', '1381', '14281', '15057', '63226', '7657', '7754',\n",
    "                        '9424', '26239', '2629', '28213', '25231', '16588', '26164',\n",
    "                        '16963', '20087', '50925']\n",
    "liver_injury_files = ['12347', '13921', '16776', '17190', '17806', '1900', '1960', '20118',\n",
    "                     '20135', '2937', '3568', '34401', '57988', '4813', '55240', '21050', '40633',\n",
    "                    '10163', '10430', '12198', '1278', '12967', '13563', '13698', '14388',\n",
    "                    '15141', '15480', '16100', '15385', '23415', '24461', '25306',\n",
    "                    '25957', '16602', '37119']\n",
    "spleen_injury_files = ['10132', '10295', '10503', '10925', '14784', '64632', '6990', '7369',\n",
    "                       '9980','17178', '18001', '15185', '22595', '27303', '31220', '27575',\n",
    "                        '10051', '10127', '1060', '10683', '11021', '8094', '8964', '902', \n",
    "                        '11664', '11834', '12698', '13517', '11046', '14178', '15089', '16343',\n",
    "                      '15620', '18653']\n",
    "no_injury_files = ['10005', '10007', '10026', '10082', '10104', '9961', '9860', '9951', '9835',\n",
    "                  '9961', '14251', '14065', '14063', '42924', '46003', '48508', '50805', '50759',\n",
    "                  '1144', '11030', '10565', '11452', '11770', '11793', '27910', '30396', '32565',\n",
    "                  '15398']\n",
    "\n",
    "# Each file corresponds to a unique patient in the \"test_images\" directory.\n",
    "test_files_used = ['48843', '50046', '63706']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b91119b",
   "metadata": {
    "papermill": {
     "duration": 0.023725,
     "end_time": "2023-08-31T10:55:06.609546",
     "exception": false,
     "start_time": "2023-08-31T10:55:06.585821",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Method that converts dicom images into a single volume(nifti)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8330e85d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:06.658602Z",
     "iopub.status.busy": "2023-08-31T10:55:06.658285Z",
     "iopub.status.idle": "2023-08-31T10:55:06.667967Z",
     "shell.execute_reply": "2023-08-31T10:55:06.666993Z"
    },
    "papermill": {
     "duration": 0.036615,
     "end_time": "2023-08-31T10:55:06.669970",
     "exception": false,
     "start_time": "2023-08-31T10:55:06.633355",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_nifti_files(files_used, folder_to_check):\n",
    "    for patient_file in files_used:\n",
    "        nested_folders = os.listdir(os.path.join(root_dir, f\"{folder_to_check}/{patient_file}\"))\n",
    "        \n",
    "        for nested_folder in nested_folders:\n",
    "            path_to_nested_folder = os.path.join(root_dir, f\"{folder_to_check}/{patient_file}\", nested_folder)\n",
    "            files = glob(path_to_nested_folder + '/*')\n",
    "            number_of_files = int(len(files))\n",
    "            \n",
    "            if number_of_files < 3:\n",
    "                settings.disable_validate_slicecount()\n",
    "                \n",
    "            # Adding modularity attribute\n",
    "            for file in sorted(files):\n",
    "                series_number = os.path.splitext(os.path.basename(os.path.normpath(file)))[0]\n",
    "                ct = pydicom.dcmread(file)\n",
    "                ct.Modality = 'CT'\n",
    "                # make directory in working dir to store new files\n",
    "                output_dir = os.path.join(working_dir, 'dicom', nested_folder)\n",
    "                os.makedirs(output_dir, exist_ok=True)\n",
    "                # save files \n",
    "                ct.save_as(os.path.join(output_dir, f'{series_number}.dcm'))\n",
    "            # make directory to store nifti files\n",
    "            nifti_dir = os.path.join(working_dir, 'nifti')\n",
    "            os.makedirs(nifti_dir, exist_ok=True)\n",
    "            # convert the files in the dicom folder to nifti file format.\n",
    "            containing_dcm = os.path.join(working_dir, 'dicom', nested_folder)\n",
    "            dicom2nifti.dicom_series_to_nifti(containing_dcm, os.path.join(nifti_dir, nested_folder + '.nii.gz'))\n",
    "            # delete dicom files after generating nifti files.\n",
    "            shutil.rmtree(os.path.join(working_dir, 'dicom', nested_folder))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fceed8b9",
   "metadata": {
    "papermill": {
     "duration": 0.023938,
     "end_time": "2023-08-31T10:55:06.717826",
     "exception": false,
     "start_time": "2023-08-31T10:55:06.693888",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Specifying train, test and evaluation nifti files "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c472e59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:06.767773Z",
     "iopub.status.busy": "2023-08-31T10:55:06.766854Z",
     "iopub.status.idle": "2023-08-31T10:55:07.189685Z",
     "shell.execute_reply": "2023-08-31T10:55:07.188716Z"
    },
    "papermill": {
     "duration": 0.450773,
     "end_time": "2023-08-31T10:55:07.192241",
     "exception": false,
     "start_time": "2023-08-31T10:55:06.741468",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_path = os.sep.join(['/kaggle', 'working', 'nifti'])\n",
    "\n",
    "# injuries\n",
    "injury_names =['bowel', 'extravasation', 'kidney', 'liver', 'spleen']\n",
    "#injury_names = ['kidney']\n",
    "\n",
    "bowel_train_images = [\n",
    "    # no bowel injury, class 0\n",
    "    '27573.nii.gz',\n",
    "    '53234.nii.gz',\n",
    "    '64202.nii.gz',\n",
    "    '1119.nii.gz',\n",
    "    '55528.nii.gz',\n",
    "    '44849.nii.gz',\n",
    "    '29995.nii.gz',\n",
    "    '29700.nii.gz',\n",
    "    '2731.nii.gz',\n",
    "    '33303.nii.gz',\n",
    "    '64587.nii.gz',\n",
    "    '18685.nii.gz',\n",
    "    '40374.nii.gz',\n",
    "    '47578.nii.gz',\n",
    "    '11416.nii.gz',\n",
    "    '60509.nii.gz',\n",
    "    '5576.nii.gz',\n",
    "    '2003.nii.gz',\n",
    "    '42932.nii.gz',\n",
    "    '18667.nii.gz',\n",
    "    # bowel injury, class 1\n",
    "    '63574.nii.gz',\n",
    "    '21228.nii.gz',\n",
    "    '24276.nii.gz',\n",
    "    '4486.nii.gz',\n",
    "    '16882.nii.gz',\n",
    "    '44685.nii.gz',\n",
    "    '65185.nii.gz',\n",
    "    '46118.nii.gz',\n",
    "    '21078.nii.gz',\n",
    "    '39434.nii.gz',\n",
    "    '64814.nii.gz',\n",
    "    '60138.nii.gz',\n",
    "    '13768.nii.gz',\n",
    "    '17182.nii.gz',\n",
    "    '17217.nii.gz',\n",
    "    '37324.nii.gz',\n",
    "    '38658.nii.gz',\n",
    "    '62600.nii.gz',\n",
    "    '12288.nii.gz',\n",
    "    '35173.nii.gz',\n",
    "    '29294.nii.gz',\n",
    "    '6439.nii.gz',\n",
    "    '34169.nii.gz',\n",
    "    '44615.nii.gz',\n",
    "    '63574.nii.gz',\n",
    "    '21228.nii.gz',\n",
    "    '24276.nii.gz',\n",
    "    '4486.nii.gz',\n",
    "    '15586.nii.gz',\n",
    "    '49474.nii.gz',\n",
    "    '6027.nii.gz',\n",
    "    '55120.nii.gz',\n",
    "    '21811.nii.gz',\n",
    "    '23769.nii.gz',\n",
    "    '53154.nii.gz',\n",
    "    '15276.nii.gz'\n",
    "]\n",
    "bowel_train_images = [os.sep.join([data_path, f]) for f in bowel_train_images]  \n",
    "bowel_train_labels = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
    "                               1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=np.int64)\n",
    "bowel_train_labels = torch.nn.functional.one_hot(torch.as_tensor(bowel_train_labels), 2).float()\n",
    "\n",
    "bowel_test_images = [\n",
    "    # no bowel injury, class 0\n",
    "    '11416.nii.gz',\n",
    "    '60509.nii.gz',\n",
    "    '29591.nii.gz',\n",
    "    '47210.nii.gz',\n",
    "    '13560.nii.gz',\n",
    "    '60588.nii.gz',\n",
    "    '55528.nii.gz',\n",
    "    '44849.nii.gz',\n",
    "    # bowel injury, class 1\n",
    "    '63574.nii.gz',\n",
    "    '21228.nii.gz',\n",
    "    '24276.nii.gz',\n",
    "    '4486.nii.gz',\n",
    "    '37324.nii.gz',\n",
    "    '38658.nii.gz',\n",
    "    '49474.nii.gz',\n",
    "    '6027.nii.gz'\n",
    "]\n",
    "\n",
    "bowel_test_images = [os.sep.join([data_path, f]) for f in bowel_test_images]\n",
    "bowel_test_labels = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], dtype=np.int64)\n",
    "bowel_test_labels = torch.nn.functional.one_hot(torch.as_tensor(bowel_test_labels), 2).float()\n",
    "\n",
    "bowel_evaluation_images = [\n",
    "    # no bowel injury, class 0\n",
    "    '29700.nii.gz',\n",
    "    '2731.nii.gz',\n",
    "    '54756.nii.gz',\n",
    "    '999.nii.gz',\n",
    "    '26866.nii.gz',\n",
    "    '37533.nii.gz',\n",
    "    '47578.nii.gz',\n",
    "    '11416.nii.gz',\n",
    "    # bowel injury, class 1\n",
    "    '21228.nii.gz',\n",
    "    '24276.nii.gz',\n",
    "    '15586.nii.gz',\n",
    "    '49474.nii.gz',\n",
    "    '6027.nii.gz',\n",
    "    '55120.nii.gz',\n",
    "    '13768.nii.gz',\n",
    "    '17182.nii.gz',\n",
    "]\n",
    "bowel_evaluation_images = [os.sep.join([data_path, f]) for f in bowel_evaluation_images]  \n",
    "bowel_evaluation_labels = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], dtype=np.int64)\n",
    "bowel_evaluation_labels = torch.nn.functional.one_hot(torch.as_tensor(bowel_evaluation_labels), 2).float()\n",
    "\n",
    "\n",
    "extravasation_train_images = [\n",
    "    # no extravasation injury, class 0\n",
    "    '27573.nii.gz',\n",
    "    '53234.nii.gz',\n",
    "    '64202.nii.gz',\n",
    "    '1119.nii.gz',\n",
    "    '55528.nii.gz',\n",
    "    '44849.nii.gz',\n",
    "    '29995.nii.gz',\n",
    "    '29700.nii.gz',\n",
    "    '2731.nii.gz',\n",
    "    '33303.nii.gz',\n",
    "    '64587.nii.gz',\n",
    "    '18685.nii.gz',\n",
    "    '40374.nii.gz',\n",
    "    '47578.nii.gz',\n",
    "    '11416.nii.gz',\n",
    "    '60509.nii.gz',\n",
    "    '5576.nii.gz',\n",
    "    '2003.nii.gz',\n",
    "    '42932.nii.gz',\n",
    "    '18667.nii.gz',\n",
    "    # extravasation injury, class 1\n",
    "    '37630.nii.gz',\n",
    "    '53257.nii.gz',\n",
    "    '3750.nii.gz',\n",
    "    '47364.nii.gz',\n",
    "    '21338.nii.gz',\n",
    "    '45638.nii.gz',\n",
    "    '60750.nii.gz',\n",
    "    '62696.nii.gz',\n",
    "    '8631.nii.gz',\n",
    "    '53973.nii.gz',\n",
    "    '24485.nii.gz',\n",
    "    '16399.nii.gz',\n",
    "    '56790.nii.gz',\n",
    "    '48517.nii.gz',\n",
    "    '53156.nii.gz',\n",
    "    '10525.nii.gz',\n",
    "    '14945.nii.gz',\n",
    "    '29647.nii.gz',\n",
    "    '65369.nii.gz',\n",
    "    '56431.nii.gz',\n",
    "    '15870.nii.gz',\n",
    "    '28676.nii.gz',\n",
    "    '1126.nii.gz',\n",
    "    '38305.nii.gz',\n",
    "    '23209.nii.gz',\n",
    "    '64737.nii.gz',\n",
    "    '12196.nii.gz',\n",
    "    '21576.nii.gz',\n",
    "    '29304.nii.gz',\n",
    "    '35443.nii.gz',\n",
    "    '42688.nii.gz',\n",
    "    '1989.nii.gz',\n",
    "    '29661.nii.gz',\n",
    "    '4654.nii.gz',\n",
    "    '25323.nii.gz',\n",
    "    '54527.nii.gz'\n",
    "]\n",
    "extravasation_train_images = [os.sep.join([data_path, f]) for f in extravasation_train_images]  \n",
    "extravasation_train_labels = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
    "                                       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=np.int64)\n",
    "extravasation_train_labels = torch.nn.functional.one_hot(torch.as_tensor(extravasation_train_labels), 2).float()\n",
    "extravasation_test_images = [\n",
    "    # no extravasation injury, class 0\n",
    "    '29700.nii.gz',\n",
    "    '2731.nii.gz',\n",
    "    '33303.nii.gz',\n",
    "    '64587.nii.gz',\n",
    "    '29591.nii.gz',\n",
    "    '47210.nii.gz',\n",
    "    '13560.nii.gz',\n",
    "    '60588.nii.gz',\n",
    "    # extravasation injury, class 1\n",
    "    '10525.nii.gz',\n",
    "    '14945.nii.gz',\n",
    "    '29647.nii.gz',\n",
    "    '65369.nii.gz',\n",
    "    '37630.nii.gz',\n",
    "    '53257.nii.gz',\n",
    "    '3750.nii.gz',\n",
    "    '47364.nii.gz'\n",
    "]\n",
    "\n",
    "extravasation_test_images = [os.sep.join([data_path, f]) for f in extravasation_test_images]\n",
    "extravasation_test_labels = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], dtype=np.int64)\n",
    "extravasation_test_labels = torch.nn.functional.one_hot(torch.as_tensor(extravasation_test_labels), 2).float()\n",
    "\n",
    "extravasation_evaluation_images = [\n",
    "    # no extravasation injury, class 0\n",
    "    '2003.nii.gz',\n",
    "    '2731.nii.gz',\n",
    "    '33303.nii.gz',\n",
    "    '64587.nii.gz',\n",
    "    '18685.nii.gz',\n",
    "    '54756.nii.gz',\n",
    "    '999.nii.gz',\n",
    "    '26866.nii.gz',\n",
    "    '37533.nii.gz',\n",
    "    # extravasation injury, class 1\n",
    "    '35443.nii.gz',\n",
    "    '42688.nii.gz',\n",
    "    '16399.nii.gz',\n",
    "    '56790.nii.gz',\n",
    "    '48517.nii.gz',\n",
    "    '21338.nii.gz',\n",
    "    '45638.nii.gz',\n",
    "    '60750.nii.gz',\n",
    "    '62696.nii.gz'\n",
    "]\n",
    "extravasation_evaluation_images = [os.sep.join([data_path, f]) for f in extravasation_evaluation_images]  \n",
    "extravasation_evaluation_labels = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=np.int64)\n",
    "extravasation_evaluation_labels = torch.nn.functional.one_hot(torch.as_tensor(extravasation_evaluation_labels), 2).float()\n",
    "\n",
    "\n",
    "kidney_train_images = [\n",
    "    # no kidney injury, class 0\n",
    "    '27573.nii.gz',\n",
    "    '53234.nii.gz',\n",
    "    '64202.nii.gz',\n",
    "    '1119.nii.gz',\n",
    "    '55528.nii.gz',\n",
    "    '44849.nii.gz',\n",
    "    '29995.nii.gz',\n",
    "    '29700.nii.gz',\n",
    "    '2731.nii.gz',\n",
    "    '33303.nii.gz',\n",
    "    '64587.nii.gz',\n",
    "    '18685.nii.gz',\n",
    "    # Low grade kidney injury, class 1\n",
    "    '39373.nii.gz',\n",
    "    '53429.nii.gz',\n",
    "    '10523.nii.gz',\n",
    "    '6366.nii.gz',\n",
    "    '43200.nii.gz',\n",
    "    '28997.nii.gz',\n",
    "    '13131.nii.gz',\n",
    "    '49511.nii.gz',\n",
    "    '6910.nii.gz', \n",
    "    '28566.nii.gz',\n",
    "    '42511.nii.gz',\n",
    "    '59048.nii.gz',\n",
    "    '44803.nii.gz',\n",
    "    '44771.nii.gz',\n",
    "    '50158.nii.gz',\n",
    "    '38081.nii.gz',\n",
    "    '45575.nii.gz',\n",
    "    '31273.nii.gz',\n",
    "    '63028.nii.gz',\n",
    "    '56068.nii.gz',\n",
    "    '56629.nii.gz',\n",
    "    '45305.nii.gz',\n",
    "    # High grade kidney injury, class 2\n",
    "    '49881.nii.gz',\n",
    "    '8188.nii.gz',\n",
    "    '17227.nii.gz',\n",
    "    '57250.nii.gz',\n",
    "    '21235.nii.gz',\n",
    "    '43565.nii.gz',\n",
    "    '16724.nii.gz',\n",
    "    '22173.nii.gz',\n",
    "    '36927.nii.gz',\n",
    "    '21227.nii.gz',\n",
    "    '3244.nii.gz',\n",
    "    '23993.nii.gz',\n",
    "    '25886.nii.gz',\n",
    "    '12274.nii.gz',\n",
    "    '41008.nii.gz',\n",
    "    '36139.nii.gz',\n",
    "    '77.nii.gz',\n",
    "    '32893.nii.gz',\n",
    "    '17269.nii.gz',\n",
    "    '6223.nii.gz',\n",
    "    '30805.nii.gz',\n",
    "    '18249.nii.gz'\n",
    "]\n",
    "kidney_train_images = [os.sep.join([data_path, f]) for f in kidney_train_images]  \n",
    "kidney_train_labels = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, \n",
    "                                 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], dtype=np.int64)\n",
    "kidney_train_labels = torch.nn.functional.one_hot(torch.as_tensor(kidney_train_labels), 3).float()\n",
    "\n",
    "kidney_test_images = [\n",
    "    # no kidney injury, class 0\n",
    "    '29591.nii.gz',\n",
    "    '47210.nii.gz',\n",
    "    '13560.nii.gz',\n",
    "    '60588.nii.gz',\n",
    "    # low grade kidney injury, class 1\n",
    "    '44771.nii.gz',\n",
    "    '50158.nii.gz',\n",
    "    '38081.nii.gz',\n",
    "    '45575.nii.gz',\n",
    "    # high grade kidney injury, class 2\n",
    "    '12274.nii.gz',\n",
    "    '41008.nii.gz',\n",
    "    '36139.nii.gz',\n",
    "    '77.nii.gz'\n",
    "]\n",
    "\n",
    "kidney_test_images = [os.sep.join([data_path, f]) for f in kidney_test_images]\n",
    "kidney_test_labels = np.array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2], dtype=np.int64)\n",
    "kidney_test_labels = torch.nn.functional.one_hot(torch.as_tensor(kidney_test_labels), 3).float()\n",
    "\n",
    "kidney_evaluation_images = [\n",
    "    # no kidney injury, class 0\n",
    "    '54756.nii.gz',\n",
    "    '999.nii.gz',\n",
    "    '26866.nii.gz',\n",
    "    '37533.nii.gz',\n",
    "    '2731.nii.gz',\n",
    "    '33303.nii.gz',\n",
    "    # low grade kidney injury, class 1\n",
    "    '31273.nii.gz',\n",
    "    '63028.nii.gz',\n",
    "    '56068.nii.gz',\n",
    "    '56629.nii.gz',\n",
    "    '59048.nii.gz',\n",
    "    '44803.nii.gz',\n",
    "    # high grade kidney injury, class 2\n",
    "    '32893.nii.gz',\n",
    "    '17269.nii.gz',\n",
    "    '6223.nii.gz',\n",
    "    '30805.nii.gz',\n",
    "    '17227.nii.gz',\n",
    "    '57250.nii.gz'\n",
    "]\n",
    "kidney_evaluation_images = [os.sep.join([data_path, f]) for f in kidney_evaluation_images]  \n",
    "kidney_evaluation_labels = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2], dtype=np.int64)\n",
    "kidney_evaluation_labels = torch.nn.functional.one_hot(torch.as_tensor(kidney_evaluation_labels), 3).float()\n",
    "\n",
    "liver_train_images = [\n",
    "    # no liver injury, class 0\n",
    "    '27573.nii.gz',\n",
    "    '53234.nii.gz',\n",
    "    '64202.nii.gz',\n",
    "    '1119.nii.gz',\n",
    "    '55528.nii.gz',\n",
    "    '44849.nii.gz',\n",
    "    '29995.nii.gz',\n",
    "    '29700.nii.gz',\n",
    "    '2731.nii.gz',\n",
    "    '33303.nii.gz',\n",
    "    '64587.nii.gz',\n",
    "    '18685.nii.gz',\n",
    "    # low grade liver injury, class 1\n",
    "    '6738.nii.gz',\n",
    "    '6417.nii.gz',\n",
    "    '55621.nii.gz',\n",
    "    '52122.nii.gz',\n",
    "    '52259.nii.gz',\n",
    "    '56685.nii.gz',\n",
    "    '47009.nii.gz',\n",
    "    '27583.nii.gz',\n",
    "    '781.nii.gz',\n",
    "    '34096.nii.gz',\n",
    "    '17137.nii.gz',\n",
    "    '17557.nii.gz',\n",
    "    '7650.nii.gz',\n",
    "    '53750.nii.gz',\n",
    "    '40492.nii.gz',\n",
    "    '12070.nii.gz',\n",
    "    '11550.nii.gz',\n",
    "    '34040.nii.gz',\n",
    "    '12312.nii.gz',\n",
    "    '39579.nii.gz',\n",
    "    '47601.nii.gz',\n",
    "    '15856.nii.gz',\n",
    "    # High grade liver injury, class 2\n",
    "    '14440.nii.gz',\n",
    "    '54841.nii.gz',\n",
    "    '13285.nii.gz',\n",
    "    '11403.nii.gz',\n",
    "    '8242.nii.gz',\n",
    "    '6536.nii.gz',\n",
    "    '2473.nii.gz',\n",
    "    '6871.nii.gz',\n",
    "    '1952.nii.gz',\n",
    "    '1796.nii.gz',\n",
    "    '23671.nii.gz',\n",
    "    '48371.nii.gz',\n",
    "    '41002.nii.gz',\n",
    "    '13901.nii.gz',\n",
    "    '16384.nii.gz',\n",
    "    '17196.nii.gz',\n",
    "    '63081.nii.gz',\n",
    "    '45401.nii.gz',\n",
    "    '12584.nii.gz',\n",
    "    '36073.nii.gz',\n",
    "    '23522.nii.gz',\n",
    "    '27391.nii.gz'\n",
    "]\n",
    "liver_train_images = [os.sep.join([data_path, f]) for f in liver_train_images]  \n",
    "liver_train_labels = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
    "                               1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], dtype=np.int64)\n",
    "liver_train_labels = torch.nn.functional.one_hot(torch.as_tensor(liver_train_labels), 3).float()\n",
    "\n",
    "liver_test_images = [\n",
    "    # no liver injury, class 0\n",
    "    '29591.nii.gz',\n",
    "    '47210.nii.gz',\n",
    "    '13560.nii.gz',\n",
    "    '60588.nii.gz',\n",
    "    '64202.nii.gz',\n",
    "    '1119.nii.gz',\n",
    "    # low grade liver injury, class 1\n",
    "    '17557.nii.gz',\n",
    "    '7650.nii.gz',\n",
    "    '53750.nii.gz',\n",
    "    '40492.nii.gz',\n",
    "    '781.nii.gz',\n",
    "    '34096.nii.gz',\n",
    "    # high grade liver injury, class 2\n",
    "    '8242.nii.gz',\n",
    "    '6536.nii.gz',\n",
    "    '2473.nii.gz',\n",
    "    '6871.nii.gz',\n",
    "    '16384.nii.gz',\n",
    "    '17196.nii.gz',\n",
    "    \n",
    "]\n",
    "\n",
    "liver_test_images = [os.sep.join([data_path, f]) for f in liver_test_images]\n",
    "liver_test_labels = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2], dtype=np.int64)\n",
    "liver_test_labels = torch.nn.functional.one_hot(torch.as_tensor(liver_test_labels), 3).float()\n",
    "\n",
    "liver_evaluation_images = [\n",
    "    # no liver injury, class 0\n",
    "    '54756.nii.gz',\n",
    "    '999.nii.gz',\n",
    "    '26866.nii.gz',\n",
    "    '37533.nii.gz',\n",
    "    '2731.nii.gz',\n",
    "    '33303.nii.gz',\n",
    "    # low grade liver injury, class 1\n",
    "    '55621.nii.gz',\n",
    "    '52122.nii.gz',\n",
    "    '52259.nii.gz',\n",
    "    '56685.nii.gz',\n",
    "    '53750.nii.gz',\n",
    "    '40492.nii.gz',\n",
    "    # high grade liver injury, class 1\n",
    "    '45401.nii.gz',\n",
    "    '12584.nii.gz',\n",
    "    '36073.nii.gz',\n",
    "    '23522.nii.gz',\n",
    "    '14440.nii.gz',\n",
    "    '54841.nii.gz',\n",
    "]\n",
    "liver_evaluation_images = [os.sep.join([data_path, f]) for f in liver_evaluation_images]  \n",
    "liver_evaluation_labels = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2], dtype=np.int64)\n",
    "liver_evaluation_labels = torch.nn.functional.one_hot(torch.as_tensor(liver_evaluation_labels), 3).float()\n",
    "\n",
    "\n",
    "spleen_train_images = [\n",
    "    # no spleen injury, class 0\n",
    "    '27573.nii.gz',\n",
    "    '53234.nii.gz',\n",
    "    '64202.nii.gz',\n",
    "    '1119.nii.gz',\n",
    "    '55528.nii.gz',\n",
    "    '44849.nii.gz',\n",
    "    '29995.nii.gz',\n",
    "    '29700.nii.gz',\n",
    "    '2731.nii.gz',\n",
    "    '33303.nii.gz',\n",
    "    '64587.nii.gz',\n",
    "    '18685.nii.gz',\n",
    "    # low grade spleen injury, class 1\n",
    "    '64776.nii.gz',\n",
    "    '60031.nii.gz',\n",
    "    '37456.nii.gz',\n",
    "    '15777.nii.gz',\n",
    "    '50172.nii.gz',\n",
    "    '12625.nii.gz',\n",
    "    '58443.nii.gz',\n",
    "    '3716.nii.gz',\n",
    "    '26599.nii.gz',\n",
    "    '17486.nii.gz',\n",
    "    '5060.nii.gz',\n",
    "    '59240.nii.gz',\n",
    "    '52163.nii.gz',\n",
    "    '19484.nii.gz',\n",
    "    '29137.nii.gz',\n",
    "    '57414.nii.gz',\n",
    "    '14324.nii.gz',\n",
    "    '59366.nii.gz',\n",
    "    '2180.nii.gz',\n",
    "    '29529.nii.gz',\n",
    "    '17000.nii.gz',\n",
    "    '53380.nii.gz',\n",
    "    # high grade spleen injury, class 2\n",
    "    '1157.nii.gz',\n",
    "    '40214.nii.gz',\n",
    "    '10273.nii.gz',\n",
    "    '52057.nii.gz',\n",
    "    '10518.nii.gz',\n",
    "    '10410.nii.gz',\n",
    "    '19713.nii.gz',\n",
    "    '12055.nii.gz',\n",
    "    '22975.nii.gz',\n",
    "    '43936.nii.gz',\n",
    "    '60534.nii.gz',\n",
    "    '40466.nii.gz',\n",
    "    '22256.nii.gz',\n",
    "    '4930.nii.gz',\n",
    "    '24164.nii.gz',\n",
    "    '52455.nii.gz',\n",
    "    '54600.nii.gz',\n",
    "    '17835.nii.gz',\n",
    "    '4816.nii.gz',\n",
    "    '49250.nii.gz',\n",
    "    '54651.nii.gz',\n",
    "    '32849.nii.gz'\n",
    "]\n",
    "spleen_train_images = [os.sep.join([data_path, f]) for f in spleen_train_images]  \n",
    "spleen_train_labels = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2,\n",
    "                                2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], dtype=np.int64)\n",
    "spleen_train_labels = torch.nn.functional.one_hot(torch.as_tensor(spleen_train_labels), 3).float()\n",
    "\n",
    "spleen_test_images = [\n",
    "    # no spleen injury, class 0\n",
    "    '29591.nii.gz',\n",
    "    '47210.nii.gz',\n",
    "    '13560.nii.gz',\n",
    "    '60588.nii.gz',\n",
    "    '53234.nii.gz',\n",
    "    '64202.nii.gz',\n",
    "    # low grade spleen injury, class 1\n",
    "    '12625.nii.gz',\n",
    "    '58443.nii.gz',\n",
    "    '2180.nii.gz',\n",
    "    '29529.nii.gz',\n",
    "    '17000.nii.gz',\n",
    "    '53380.nii.gz',\n",
    "    # high grade spleen injury, class 2\n",
    "    '4816.nii.gz',\n",
    "    '49250.nii.gz',\n",
    "    '54651.nii.gz',\n",
    "    '32849.nii.gz',\n",
    "    '10273.nii.gz',\n",
    "    '52057.nii.gz',\n",
    "]\n",
    "\n",
    "spleen_test_images = [os.sep.join([data_path, f]) for f in spleen_test_images]\n",
    "spleen_test_labels = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2], dtype=np.int64)\n",
    "spleen_test_labels = torch.nn.functional.one_hot(torch.as_tensor(spleen_test_labels), 3).float()\n",
    "\n",
    "spleen_evaluation_images = [\n",
    "    # no spleen injury, class 0\n",
    "    '54756.nii.gz',\n",
    "    '999.nii.gz',\n",
    "    '33303.nii.gz',\n",
    "    '64587.nii.gz',\n",
    "    '26866.nii.gz',\n",
    "    '37533.nii.gz',\n",
    "    # low grade spleen injury, class 1\n",
    "    '3716.nii.gz',\n",
    "    '26599.nii.gz',\n",
    "    '17486.nii.gz',\n",
    "    '19484.nii.gz',\n",
    "    '29137.nii.gz',\n",
    "    '5060.nii.gz',\n",
    "    # high grade spleen injury, class 2\n",
    "    '10410.nii.gz',\n",
    "    '19713.nii.gz',\n",
    "    '22256.nii.gz',\n",
    "    '4930.nii.gz',\n",
    "    '12055.nii.gz',\n",
    "    '22975.nii.gz'\n",
    "]\n",
    "spleen_evaluation_images = [os.sep.join([data_path, f]) for f in spleen_evaluation_images]  \n",
    "spleen_evaluation_labels = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2], dtype=np.int64)\n",
    "spleen_evaluation_labels = torch.nn.functional.one_hot(torch.as_tensor(spleen_evaluation_labels), 3).float()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92581111",
   "metadata": {
    "papermill": {
     "duration": 0.023728,
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     "exception": false,
     "start_time": "2023-08-31T10:55:07.216606",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Transforms applied to the nifti images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "decf005b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:07.289936Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# image transformations\n",
    "transforms = Compose([\n",
    "    ScaleIntensityRange(a_min=-200, a_max=200, b_min=0.0, b_max=1.0, clip=True),\n",
    "    EnsureChannelFirst(),\n",
    "    #Orientation(axcodes='RAS'),\n",
    "    Resize((128,128,64))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66fbb75a",
   "metadata": {
    "papermill": {
     "duration": 0.023582,
     "end_time": "2023-08-31T10:55:07.344385",
     "exception": false,
     "start_time": "2023-08-31T10:55:07.320803",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "# <div style=\"color:white;display:inline-block;border-radius:5px;background-color:#FF7F50;font-family:Nexa;overflow:hidden\"><p style=\"padding:20px;color:white;overflow:hidden;font-size:80%;letter-spacing:0.5px;margin:0\"><b> </b> Training and evaluating DenseNet264 models used to detect the injuries</p></div>\n",
    "#### Model training function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "84b97e16",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:07.393416Z",
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     "shell.execute_reply": "2023-08-31T10:55:07.414119Z"
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     "duration": 0.048846,
     "end_time": "2023-08-31T10:55:07.416931",
     "exception": false,
     "start_time": "2023-08-31T10:55:07.368085",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# train the model\n",
    "def train(injury_name):\n",
    "    \n",
    "    if injury_name == 'bowel':\n",
    "        create_nifti_files(bowel_injury_files, 'train_images')\n",
    "        train_images = bowel_train_images\n",
    "        train_labels = bowel_train_labels\n",
    "        test_images = bowel_test_images\n",
    "        test_labels = bowel_test_labels\n",
    "    elif injury_name == 'extravasation':\n",
    "        create_nifti_files(extravasation_injury_files, 'train_images')\n",
    "        train_images = extravasation_train_images\n",
    "        train_labels = extravasation_train_labels\n",
    "        test_images = extravasation_test_images\n",
    "        test_labels = extravasation_test_labels\n",
    "    elif injury_name == 'kidney':\n",
    "        create_nifti_files(kidney_injury_files, 'train_images')\n",
    "        train_images = kidney_train_images\n",
    "        train_labels = kidney_train_labels\n",
    "        test_images = kidney_test_images\n",
    "        test_labels = kidney_test_labels\n",
    "    elif injury_name == 'liver':\n",
    "        create_nifti_files(liver_injury_files, 'train_images')\n",
    "        train_images = liver_train_images\n",
    "        train_labels = liver_train_labels\n",
    "        test_images = liver_test_images\n",
    "        test_labels = liver_test_labels\n",
    "    else:\n",
    "        create_nifti_files(spleen_injury_files, 'train_images')\n",
    "        train_images = spleen_train_images\n",
    "        train_labels = spleen_train_labels\n",
    "        test_images = spleen_test_images\n",
    "        test_labels = spleen_test_labels\n",
    "\n",
    "    # create a training data loader\n",
    "    train_ds = ImageDataset(image_files=train_images, labels=train_labels, transform=transforms)\n",
    "    train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=2, pin_memory=pin_memory)\n",
    "\n",
    "    # create a testing data loader\n",
    "    test_ds = ImageDataset(image_files=test_images, labels=test_labels, transform=transforms)\n",
    "    test_loader = DataLoader(test_ds, batch_size=2, num_workers=2, pin_memory=pin_memory)\n",
    "  \n",
    "    # Create DenseNet264, CrossEntropyLoss and Adam optimizer\n",
    "    if injury_name == 'bowel' or injury_name == 'extravasation':\n",
    "        model = monai.networks.nets.DenseNet264(spatial_dims=3, in_channels=1, out_channels=2).to(device)\n",
    "    else:\n",
    "        model = monai.networks.nets.DenseNet264(spatial_dims=3, in_channels=1, out_channels=3).to(device)\n",
    "  \n",
    "    loss_function = torch.nn.CrossEntropyLoss()\n",
    "    optimizer = torch.optim.Adam(model.parameters(), 1e-5)\n",
    "  \n",
    "    # initial metrics\n",
    "    test_interval = 2\n",
    "    best_metric = -1\n",
    "    best_metric_epoch = -1\n",
    "    epoch_loss_values = []\n",
    "    metric_values = []\n",
    "    writer = SummaryWriter()\n",
    "    max_epochs = 25\n",
    "\n",
    "    for epoch in range(max_epochs):\n",
    "        print(\"-\" * 10)\n",
    "        print(f\"epoch {epoch + 1}/{max_epochs}\")\n",
    "        model.train()\n",
    "        epoch_loss = 0\n",
    "        step = 0\n",
    "\n",
    "        for batch_data in train_loader:\n",
    "            step += 1\n",
    "            inputs, labels = batch_data[0].to(device), batch_data[1].to(device)\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(inputs)\n",
    "            loss = loss_function(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            epoch_loss += loss.item()\n",
    "            epoch_len = len(train_ds) // train_loader.batch_size\n",
    "            writer.add_scalar(\"train_loss\", loss.item(), epoch_len * epoch + step)\n",
    "\n",
    "        epoch_loss /= step\n",
    "        epoch_loss_values.append(epoch_loss)\n",
    "        print(f\"epoch {epoch + 1} average train loss: {epoch_loss:.4f}\")\n",
    "\n",
    "        if (epoch + 1) % test_interval == 0:\n",
    "            model.eval()\n",
    "\n",
    "            num_correct = 0.0\n",
    "            metric_count = 0\n",
    "            for test_data in test_loader:\n",
    "                test_images, test_labels = test_data[0].to(device), test_data[1].to(device)\n",
    "                with torch.no_grad():\n",
    "                    test_outputs = model(test_images)\n",
    "                    value = torch.eq(test_outputs.argmax(dim=1), test_labels.argmax(dim=1))\n",
    "                    metric_count += len(value)\n",
    "                    num_correct += value.sum().item()\n",
    "\n",
    "            metric = num_correct / metric_count\n",
    "            metric_values.append(metric)\n",
    "\n",
    "            # Saving the model with the best metrics.\n",
    "            if metric > best_metric:\n",
    "                best_metric = metric\n",
    "                best_metric_epoch = epoch + 1\n",
    "                best_metric_model_dir = os.path.join(working_dir, 'best_metric')\n",
    "                os.makedirs(best_metric_model_dir, exist_ok=True)\n",
    "                save_path = f'/kaggle/working/best_metric/best_metric_model_for_{injury_name}.pth'\n",
    "                torch.save(model.state_dict(), save_path)\n",
    "                print(f\"Saved new best metric model for {injury_name} injuries\")\n",
    "                print(f\"Current epoch: {epoch + 1} current accuracy: {metric:.4f}\")\n",
    "                print(f\"Best accuracy: {best_metric:.4f} at epoch {best_metric_epoch}\")\n",
    "                writer.add_scalar(\"test_accuracy\", metric, epoch + 1)\n",
    "\n",
    "            # Early stopping \n",
    "            if best_metric >= 0.75:\n",
    "                break\n",
    "            \n",
    "                \n",
    "    print(f'Training completed for model concerning {injury_name} injuries.')\n",
    "    print(f\"Best metric is: {best_metric:.4f} at epoch: {best_metric_epoch}\")\n",
    "    writer.close()\n",
    "\n",
    "    # evaluate the trained model\n",
    "    evaluate(injury_name) \n",
    "    # delete files after training\n",
    "    if injury_name == 'kidney':\n",
    "        wipe_directory(f'/kaggle/working/nifti')\n",
    "        create_nifti_files(no_injury_files, 'train_images')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba4ecb81",
   "metadata": {
    "papermill": {
     "duration": 0.023575,
     "end_time": "2023-08-31T10:55:07.464332",
     "exception": false,
     "start_time": "2023-08-31T10:55:07.440757",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Model evaluation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "56e04816",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:07.516839Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# evaluate the models\n",
    "def evaluate(injury_name):\n",
    "    # create evaluation data loader\n",
    "    if injury_name == 'bowel':\n",
    "        val_images = bowel_evaluation_images\n",
    "        val_labels = bowel_evaluation_labels\n",
    "    elif injury_name == 'extravasation':\n",
    "        val_images = extravasation_evaluation_images\n",
    "        val_labels = extravasation_evaluation_labels\n",
    "    elif injury_name == 'kidney':\n",
    "        val_images = kidney_evaluation_images\n",
    "        val_labels = kidney_evaluation_labels\n",
    "    elif injury_name == 'liver':\n",
    "        val_images = liver_evaluation_images\n",
    "        val_labels = liver_evaluation_labels\n",
    "    else:\n",
    "        val_images = spleen_evaluation_images\n",
    "        val_labels = spleen_evaluation_labels\n",
    "    \n",
    "    val_ds = ImageDataset(image_files=val_images, labels=val_labels, transform=transforms)\n",
    "    val_loader = DataLoader(val_ds, batch_size=1, num_workers=2, pin_memory=pin_memory)\n",
    "    \n",
    "    # Create DenseNet121\n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "    if injury_name == 'bowel' or injury_name == 'extravasation':\n",
    "        model = monai.networks.nets.DenseNet264(spatial_dims=3, in_channels=1, out_channels=2).to(device)\n",
    "    else:\n",
    "        model = monai.networks.nets.DenseNet264(spatial_dims=3, in_channels=1, out_channels=3).to(device)\n",
    "\n",
    "    save_path = f'/kaggle/working/best_metric/best_metric_model_for_{injury_name}.pth'\n",
    "    model.load_state_dict(torch.load(save_path))\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        num_correct = 0.0\n",
    "        metric_count = 0\n",
    "        evaluation_dir = os.path.join(working_dir, f'{injury_name}_evaluation')\n",
    "        os.makedirs(evaluation_dir, exist_ok=True)\n",
    "        saver = CSVSaver(output_dir=evaluation_dir)\n",
    "        for val_data in val_loader:\n",
    "            val_images, val_labels = val_data[0].to(device), val_data[1].to(device)\n",
    "            val_outputs = model(val_images)\n",
    "            value = torch.eq(val_outputs.argmax(dim=1), val_labels.argmax(dim=1))\n",
    "            probabilities = torch.softmax(val_outputs, dim=1)\n",
    "            metric_count += len(value)\n",
    "            num_correct += value.sum().item()\n",
    "            saver.save_batch(probabilities, val_images.meta)\n",
    "        metric = num_correct / metric_count\n",
    "        print(f\"Evaluation metric for the {injury_name} injury model is:\", metric)\n",
    "        saver.finalize()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02d16bce",
   "metadata": {
    "papermill": {
     "duration": 0.023798,
     "end_time": "2023-08-31T10:55:07.578263",
     "exception": false,
     "start_time": "2023-08-31T10:55:07.554465",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Training a model for each injury"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3c76b820",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T10:55:07.627760Z",
     "iopub.status.busy": "2023-08-31T10:55:07.626919Z",
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     "shell.execute_reply": "2023-08-31T13:33:02.965817Z"
    },
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     "exception": false,
     "start_time": "2023-08-31T10:55:07.601945",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n",
      "epoch 1/100\n",
      "epoch 1 average train loss: 0.7713\n",
      "----------\n",
      "epoch 2/100\n",
      "epoch 2 average train loss: 0.6524\n",
      "Saved new best metric model for bowel injuries\n",
      "Current epoch: 2 current accuracy: 0.8750\n",
      "Best accuracy: 0.8750 at epoch 2\n",
      "Training completed for model concerning bowel injuries.\n",
      "Best metric is: 0.8750 at epoch: 2\n",
      "Evaluation metric for the bowel injury model is: 0.75\n",
      "----------\n",
      "epoch 1/100\n",
      "epoch 1 average train loss: 0.7154\n",
      "----------\n",
      "epoch 2/100\n",
      "epoch 2 average train loss: 0.6584\n",
      "Saved new best metric model for extravasation injuries\n",
      "Current epoch: 2 current accuracy: 0.8125\n",
      "Best accuracy: 0.8125 at epoch 2\n",
      "Training completed for model concerning extravasation injuries.\n",
      "Best metric is: 0.8125 at epoch: 2\n",
      "Evaluation metric for the extravasation injury model is: 0.5\n",
      "----------\n",
      "epoch 1/100\n",
      "epoch 1 average train loss: 1.1344\n",
      "----------\n",
      "epoch 2/100\n",
      "epoch 2 average train loss: 1.0721\n",
      "Saved new best metric model for kidney injuries\n",
      "Current epoch: 2 current accuracy: 0.4167\n",
      "Best accuracy: 0.4167 at epoch 2\n",
      "----------\n",
      "epoch 3/100\n",
      "epoch 3 average train loss: 1.0180\n",
      "----------\n",
      "epoch 4/100\n",
      "epoch 4 average train loss: 0.9820\n",
      "Saved new best metric model for kidney injuries\n",
      "Current epoch: 4 current accuracy: 0.5833\n",
      "Best accuracy: 0.5833 at epoch 4\n",
      "Training completed for model concerning kidney injuries.\n",
      "Best metric is: 0.5833 at epoch: 4\n",
      "Evaluation metric for the kidney injury model is: 0.6111111111111112\n",
      "----------\n",
      "epoch 1/100\n",
      "epoch 1 average train loss: 1.1994\n",
      "----------\n",
      "epoch 2/100\n",
      "epoch 2 average train loss: 1.0784\n",
      "Saved new best metric model for liver injuries\n",
      "Current epoch: 2 current accuracy: 0.7222\n",
      "Best accuracy: 0.7222 at epoch 2\n",
      "Training completed for model concerning liver injuries.\n",
      "Best metric is: 0.7222 at epoch: 2\n",
      "Evaluation metric for the liver injury model is: 0.6666666666666666\n",
      "----------\n",
      "epoch 1/100\n",
      "epoch 1 average train loss: 1.0973\n",
      "----------\n",
      "epoch 2/100\n",
      "epoch 2 average train loss: 1.0249\n",
      "Saved new best metric model for spleen injuries\n",
      "Current epoch: 2 current accuracy: 0.6111\n",
      "Best accuracy: 0.6111 at epoch 2\n",
      "Training completed for model concerning spleen injuries.\n",
      "Best metric is: 0.6111 at epoch: 2\n",
      "Evaluation metric for the spleen injury model is: 0.5\n"
     ]
    }
   ],
   "source": [
    "create_nifti_files(no_injury_files, 'train_images')\n",
    "\n",
    "for injury in injury_names:\n",
    "    train(injury)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cc3cd4a",
   "metadata": {
    "papermill": {
     "duration": 0.02573,
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     "start_time": "2023-08-31T13:33:03.028019",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# <div style=\"color:white;display:inline-block;border-radius:5px;background-color:#FF7F50;font-family:Nexa;overflow:hidden\"><p style=\"padding:20px;color:white;overflow:hidden;font-size:80%;letter-spacing:0.5px;margin:0\"><b> </b> Method to generate probabilities for test_images</p></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "46ade856",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-31T13:33:03.108019Z",
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   "source": [
    "def get_predictions():\n",
    "    patient_ids = [\"48843\", \"50046\", \"63706\"]\n",
    "    \n",
    "    create_nifti_files(test_files_used, 'test_images')\n",
    "    \n",
    "    test_images = [\n",
    "        '62825.nii.gz',\n",
    "        '24574.nii.gz',\n",
    "        '39279.nii.gz'\n",
    "    ]\n",
    "    test_images = [os.sep.join([data_path, f]) for f in test_images]\n",
    "    \n",
    "    test_ds = ImageDataset(image_files=test_images, transform=transforms)\n",
    "    test_loader = DataLoader(test_ds, batch_size=1, num_workers=2, pin_memory=pin_memory)\n",
    "\n",
    "    \n",
    "    for injury_name in injury_names:\n",
    "        \n",
    "        # Create DenseNet264 model\n",
    "        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "        if injury_name == 'bowel' or injury_name == 'extravasation':\n",
    "            all_results = pd.DataFrame(columns=[f\"{injury_name}_healthy\", f\"{injury_name}_injury\", \"patient_id\"])\n",
    "            \n",
    "            model = monai.networks.nets.DenseNet264(spatial_dims=3, in_channels=1, out_channels=2).to(device)\n",
    "            \n",
    "            save_path = f'/kaggle/working/best_metric/best_metric_model_for_{injury_name}.pth'\n",
    "            model.load_state_dict(torch.load(save_path))\n",
    "            model.eval()\n",
    "            \n",
    "            for index, test_image in enumerate(test_loader):\n",
    "                test_image = test_image.to(device)\n",
    "                output = model(test_image)\n",
    "                probabilities = torch.softmax(output, dim=1)\n",
    "                pattern = r\"tensor\\(\\[\\[(\\d+\\.\\d+), (\\d+\\.\\d+)\\]\\]\"\n",
    "                matches = re.findall(pattern, str(probabilities))\n",
    "                probabilities_list = [[float(match[0]), float(match[1])] for match in matches]\n",
    "                results = pd.DataFrame(probabilities_list, columns=[f\"{injury_name}_healthy\", f\"{injury_name}_injury\"])\n",
    "                results[\"patient_id\"] = patient_ids[index]\n",
    "                all_results = pd.concat([all_results, results], ignore_index=True)\n",
    "            all_results.to_csv(f'/kaggle/working/final_results/{injury_name}.csv', index=False)\n",
    "            print(all_results)\n",
    "        else:\n",
    "            all_results = pd.DataFrame(columns=[f\"{injury_name}_healthy\", f\"{injury_name}_low\", f\"{injury_name}_high\", \"patient_id\"])\n",
    "            \n",
    "            model = monai.networks.nets.DenseNet264(spatial_dims=3, in_channels=1, out_channels=3).to(device)\n",
    "\n",
    "            save_path = f'/kaggle/working/best_metric/best_metric_model_for_{injury_name}.pth'\n",
    "            model.load_state_dict(torch.load(save_path))\n",
    "            model.eval()\n",
    "            \n",
    "            for index, test_image in enumerate(test_loader):\n",
    "                test_image = test_image.to(device)\n",
    "                output = model(test_image)\n",
    "                probabilities = torch.softmax(output, dim=1)\n",
    "                pattern = r\"tensor\\(\\[\\[(\\d+\\.\\d+), (\\d+\\.\\d+), (\\d+\\.\\d+)\\]\\]\"\n",
    "                matches = re.findall(pattern, str(probabilities))\n",
    "                probabilities_list = [[float(match[0]), float(match[1]), float(match[2])] for match in matches]\n",
    "                results = pd.DataFrame(probabilities_list, columns=[f\"{injury_name}_healthy\", f\"{injury_name}_low\", f\"{injury_name}_high\"])\n",
    "                results[\"patient_id\"] = patient_ids[index]\n",
    "                all_results = pd.concat([all_results, results], ignore_index=True)\n",
    "            all_results.to_csv(f'/kaggle/working/final_results/{injury_name}.csv', index=False)\n",
    "            print(all_results)\n",
    "            \n"
   ]
  },
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   "source": [
    "# <div style=\"color:white;display:inline-block;border-radius:5px;background-color:#FF7F50;font-family:Nexa;overflow:hidden\"><p style=\"padding:20px;color:white;overflow:hidden;font-size:80%;letter-spacing:0.5px;margin:0\"><b> </b> Generating submission file</p></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "395c0551",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2023-08-31T13:33:03.208005",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   bowel_healthy  bowel_injury patient_id\n",
      "0         0.4367        0.5633      48843\n",
      "1         0.4612        0.5388      50046\n",
      "2         0.4011        0.5989      63706\n",
      "   extravasation_healthy  extravasation_injury patient_id\n",
      "0                 0.4619                0.5381      48843\n",
      "1                 0.4338                0.5662      50046\n",
      "2                 0.5311                0.4689      63706\n",
      "   kidney_healthy  kidney_low  kidney_high patient_id\n",
      "0          0.1438      0.4726       0.3836      48843\n",
      "1          0.3013      0.4178       0.2809      50046\n",
      "2          0.1498      0.5447       0.3055      63706\n",
      "   liver_healthy  liver_low  liver_high patient_id\n",
      "0         0.2426     0.3679      0.3895      48843\n",
      "1         0.3081     0.3156      0.3762      50046\n",
      "2         0.2683     0.3341      0.3976      63706\n",
      "   spleen_healthy  spleen_low  spleen_high patient_id\n",
      "0          0.1964      0.4616       0.3420      48843\n",
      "1          0.2930      0.3513       0.3557      50046\n",
      "2          0.2595      0.4029       0.3376      63706\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>bowel_healthy</th>\n",
       "      <th>bowel_injury</th>\n",
       "      <th>extravasation_healthy</th>\n",
       "      <th>extravasation_injury</th>\n",
       "      <th>kidney_healthy</th>\n",
       "      <th>kidney_low</th>\n",
       "      <th>kidney_high</th>\n",
       "      <th>liver_healthy</th>\n",
       "      <th>liver_low</th>\n",
       "      <th>liver_high</th>\n",
       "      <th>spleen_healthy</th>\n",
       "      <th>spleen_low</th>\n",
       "      <th>spleen_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48843</td>\n",
       "      <td>0.4367</td>\n",
       "      <td>0.5633</td>\n",
       "      <td>0.4619</td>\n",
       "      <td>0.5381</td>\n",
       "      <td>0.1438</td>\n",
       "      <td>0.4726</td>\n",
       "      <td>0.3836</td>\n",
       "      <td>0.2426</td>\n",
       "      <td>0.3679</td>\n",
       "      <td>0.3895</td>\n",
       "      <td>0.1964</td>\n",
       "      <td>0.4616</td>\n",
       "      <td>0.3420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50046</td>\n",
       "      <td>0.4612</td>\n",
       "      <td>0.5388</td>\n",
       "      <td>0.4338</td>\n",
       "      <td>0.5662</td>\n",
       "      <td>0.3013</td>\n",
       "      <td>0.4178</td>\n",
       "      <td>0.2809</td>\n",
       "      <td>0.3081</td>\n",
       "      <td>0.3156</td>\n",
       "      <td>0.3762</td>\n",
       "      <td>0.2930</td>\n",
       "      <td>0.3513</td>\n",
       "      <td>0.3557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63706</td>\n",
       "      <td>0.4011</td>\n",
       "      <td>0.5989</td>\n",
       "      <td>0.5311</td>\n",
       "      <td>0.4689</td>\n",
       "      <td>0.1498</td>\n",
       "      <td>0.5447</td>\n",
       "      <td>0.3055</td>\n",
       "      <td>0.2683</td>\n",
       "      <td>0.3341</td>\n",
       "      <td>0.3976</td>\n",
       "      <td>0.2595</td>\n",
       "      <td>0.4029</td>\n",
       "      <td>0.3376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient_id  bowel_healthy  bowel_injury  extravasation_healthy  \\\n",
       "0       48843         0.4367        0.5633                 0.4619   \n",
       "1       50046         0.4612        0.5388                 0.4338   \n",
       "2       63706         0.4011        0.5989                 0.5311   \n",
       "\n",
       "   extravasation_injury  kidney_healthy  kidney_low  kidney_high  \\\n",
       "0                0.5381          0.1438      0.4726       0.3836   \n",
       "1                0.5662          0.3013      0.4178       0.2809   \n",
       "2                0.4689          0.1498      0.5447       0.3055   \n",
       "\n",
       "   liver_healthy  liver_low  liver_high  spleen_healthy  spleen_low  \\\n",
       "0         0.2426     0.3679      0.3895          0.1964      0.4616   \n",
       "1         0.3081     0.3156      0.3762          0.2930      0.3513   \n",
       "2         0.2683     0.3341      0.3976          0.2595      0.4029   \n",
       "\n",
       "   spleen_high  \n",
       "0       0.3420  \n",
       "1       0.3557  \n",
       "2       0.3376  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make and store predictions\n",
    "os.makedirs('/kaggle/working/final_results', exist_ok=True)\n",
    "get_predictions()\n",
    "\n",
    "df1 = pd.read_csv('/kaggle/working/final_results/bowel.csv')\n",
    "df2 = pd.read_csv('/kaggle/working/final_results/extravasation.csv')\n",
    "df3 = pd.read_csv('/kaggle/working/final_results/kidney.csv')\n",
    "df4 = pd.read_csv('/kaggle/working/final_results/liver.csv')\n",
    "df5 = pd.read_csv('/kaggle/working/final_results/spleen.csv')\n",
    "\n",
    "# merge the two dataframes\n",
    "merged_df = df1.merge(df2[['patient_id', 'extravasation_healthy', 'extravasation_injury']], on='patient_id', how='outer') \\\n",
    "               .merge(df3[['patient_id', 'kidney_healthy', 'kidney_low', 'kidney_high']], on='patient_id', how='outer') \\\n",
    "               .merge(df4[['patient_id', 'liver_healthy', 'liver_low', 'liver_high']], on='patient_id', how='outer') \\\n",
    "               .merge(df5[['patient_id', 'spleen_healthy', 'spleen_low', 'spleen_high']], on='patient_id', how='outer')\n",
    "\n",
    "# add any_injury column\n",
    "#def has_any_injury(row):\n",
    "    #return 1 if row['bowel_injury'] > 0.5 or row['extravasation_injury'] > 0.5 or row['liver_low'] > 0.5 or \\\n",
    "    #row['liver_high'] > 0.5  or row['kidney_low'] > 0.5 or row['kidney_high'] > 0.5 or row['spleen_low'] > 0.5 \\\n",
    "    #or row['spleen_high'] > 0.5 else 0\n",
    "\n",
    "#merged_df['any_injury'] = merged_df.apply(has_any_injury, axis=1)\n",
    "#reorder columns\n",
    "merged_df = merged_df[['patient_id'] + [col for col in merged_df.columns if col != 'patient_id']]\n",
    "\n",
    "merged_df.to_csv('submission.csv', index=False)\n",
    "\n",
    "merged_df.head()"
   ]
  },
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