flair_cadec_v2_baseline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os, random\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['meddra',\n",
       " 'meddra - unedited version of edited files',\n",
       " 'original',\n",
       " 'original - unedited version of edited files',\n",
       " 'sct',\n",
       " 'sct - unedited version of edited files',\n",
       " 'text']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('datasets/CADEC/CADEC.v2/cadec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "suf = '_v2'\n",
    "\n",
    "data_save_dir = 'data'+suf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ner</th>\n",
       "      <th>meddra</th>\n",
       "      <th>counts_exploded</th>\n",
       "      <th>counts</th>\n",
       "      <th>counts_exploded_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Drug</td>\n",
       "      <td>Lipitor</td>\n",
       "      <td>1081</td>\n",
       "      <td>1073</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADR</td>\n",
       "      <td>CONCEPT_LESS</td>\n",
       "      <td>1016</td>\n",
       "      <td>266</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADR</td>\n",
       "      <td>Severe_pain</td>\n",
       "      <td>722</td>\n",
       "      <td>247</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADR</td>\n",
       "      <td>Myalgia</td>\n",
       "      <td>619</td>\n",
       "      <td>305</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ADR</td>\n",
       "      <td>Pain</td>\n",
       "      <td>292</td>\n",
       "      <td>260</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>Finding</td>\n",
       "      <td>Senility</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>Drug</td>\n",
       "      <td>Seroquel</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>Drug</td>\n",
       "      <td>Solaraze</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>Drug</td>\n",
       "      <td>Tagamet</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>Drug</td>\n",
       "      <td>testosterone</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1299 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ner        meddra  counts_exploded  counts  counts_exploded_rank\n",
       "0        Drug       Lipitor             1081    1073                     1\n",
       "1         ADR  CONCEPT_LESS             1016     266                     2\n",
       "2         ADR   Severe_pain              722     247                     3\n",
       "3         ADR       Myalgia              619     305                     4\n",
       "4         ADR          Pain              292     260                     5\n",
       "...       ...           ...              ...     ...                   ...\n",
       "1294  Finding      Senility                1       1                  1233\n",
       "1295     Drug      Seroquel                1       1                  1233\n",
       "1296     Drug      Solaraze                1       1                  1233\n",
       "1297     Drug       Tagamet                1       1                  1233\n",
       "1298     Drug  testosterone                1       1                  1233\n",
       "\n",
       "[1299 rows x 5 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meddra_counts = pd.read_csv(data_save_dir+'/meddra_counts.csv')\n",
    "meddra_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ner</th>\n",
       "      <th>meddra</th>\n",
       "      <th>counts_exploded</th>\n",
       "      <th>counts</th>\n",
       "      <th>counts_exploded_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADR</td>\n",
       "      <td>CONCEPT_LESS</td>\n",
       "      <td>1016</td>\n",
       "      <td>266</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADR</td>\n",
       "      <td>Severe_pain</td>\n",
       "      <td>722</td>\n",
       "      <td>247</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADR</td>\n",
       "      <td>Myalgia</td>\n",
       "      <td>619</td>\n",
       "      <td>305</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ADR</td>\n",
       "      <td>Pain</td>\n",
       "      <td>292</td>\n",
       "      <td>260</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ADR</td>\n",
       "      <td>Arthralgia</td>\n",
       "      <td>280</td>\n",
       "      <td>163</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>ADR</td>\n",
       "      <td>Labyrinthitis</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>ADR</td>\n",
       "      <td>Dysphagia</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>ADR</td>\n",
       "      <td>Lupus_erythematosus</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>ADR</td>\n",
       "      <td>Cataract</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>ADR</td>\n",
       "      <td>Crying_associated_with_mood</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>800 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ner                       meddra  counts_exploded  counts   \n",
       "1     ADR                 CONCEPT_LESS             1016     266  \\\n",
       "2     ADR                  Severe_pain              722     247   \n",
       "3     ADR                      Myalgia              619     305   \n",
       "4     ADR                         Pain              292     260   \n",
       "5     ADR                   Arthralgia              280     163   \n",
       "...   ...                          ...              ...     ...   \n",
       "1268  ADR                Labyrinthitis                1       1   \n",
       "1269  ADR                    Dysphagia                1       1   \n",
       "1275  ADR          Lupus_erythematosus                1       1   \n",
       "1278  ADR                     Cataract                1       1   \n",
       "1283  ADR  Crying_associated_with_mood                1       1   \n",
       "\n",
       "      counts_exploded_rank  \n",
       "1                        2  \n",
       "2                        3  \n",
       "3                        4  \n",
       "4                        5  \n",
       "5                        6  \n",
       "...                    ...  \n",
       "1268                  1233  \n",
       "1269                  1233  \n",
       "1275                  1233  \n",
       "1278                  1233  \n",
       "1283                  1233  \n",
       "\n",
       "[800 rows x 5 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meddra_counts.loc[meddra_counts.ner == 'ADR',:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Sentence[117]: \"i feel a bit drowsy & have a little blurred vision , so far no gastric problems . ive been on arthrotec 50 for over 10 years on and off , only taking it when i needed it . due to my arthritis getting progressively worse , to the point where i am in tears with the agony , gp ' s started me on 75 twice a day and i have to take it . every day for the next month to see how i get on , here goes . so far its been very good , pains almost gone , but i feel a bit weird , didnt have that when on 50 .\" → [\"i\"/O/O, \"feel\"/O/O, \"a\"/O/O, \"bit\"/B-ADR/B-Drowsy, \"drowsy\"/E-ADR/E-Drowsy, \"&\"/O/O, \"have\"/O/O, \"a\"/O/O, \"little\"/B-ADR/B-Blurred_vision_-_hazy, \"blurred\"/I-ADR/I-Blurred_vision_-_hazy, \"vision\"/E-ADR/E-Blurred_vision_-_hazy, \",\"/O/O, \"so\"/O/O, \"far\"/O/O, \"no\"/O/O, \"gastric\"/B-ADR/B-Excessive_upper_gastrointestinal_gas, \"problems\"/E-ADR/E-Excessive_upper_gastrointestinal_gas, \".\"/O/O, \"ive\"/O/O, \"been\"/O/O, \"on\"/O/O, \"arthrotec\"/S-Drug/S-Arthrotec, \"50\"/O/O, \"for\"/O/O, \"over\"/O/O, \"10\"/O/O, \"years\"/O/O, \"on\"/O/O, \"and\"/O/O, \"off\"/O/O, \",\"/O/O, \"only\"/O/O, \"taking\"/O/O, \"it\"/O/O, \"when\"/O/O, \"i\"/O/O, \"needed\"/O/O, \"it\"/O/O, \".\"/O/O, \"due\"/O/O, \"to\"/O/O, \"my\"/O/O, \"arthritis\"/S-Disease/S-Arthritis, \"getting\"/O/O, \"progressively\"/O/O, \"worse\"/O/O, \",\"/O/O, \"to\"/O/O, \"the\"/O/O, \"point\"/O/O, \"where\"/O/O, \"i\"/O/O, \"am\"/O/O, \"in\"/O/O, \"tears\"/O/O, \"with\"/O/O, \"the\"/O/O, \"agony\"/S-Symptom/S-Agony, \",\"/O/O, \"gp\"/O/O, \"'\"/O/O, \"s\"/O/O, \"started\"/O/O, \"me\"/O/O, \"on\"/O/O, \"75\"/O/O, \"twice\"/O/O, \"a\"/O/O, \"day\"/O/O, \"and\"/O/O, \"i\"/O/O, \"have\"/O/O, \"to\"/O/O, \"take\"/O/O, \"it\"/O/O, \".\"/O/O, \"every\"/O/O, \"day\"/O/O, \"for\"/O/O, \"the\"/O/O, \"next\"/O/O, \"month\"/O/O, \"to\"/O/O, \"see\"/O/O, \"how\"/O/O, \"i\"/O/O, \"get\"/O/O, \"on\"/O/O, \",\"/O/O, \"here\"/O/O, \"goes\"/O/O, \".\"/O/O, \"so\"/O/O, \"far\"/O/O, \"its\"/O/O, \"been\"/O/O, \"very\"/O/O, \"good\"/O/O, \",\"/O/O, \"pains\"/S-Symptom/S-Pain, \"almost\"/O/O, \"gone\"/O/O, \",\"/O/O, \"but\"/O/O, \"i\"/O/O, \"feel\"/B-ADR/B-Malaise, \"a\"/I-ADR/I-Malaise, \"bit\"/I-ADR/I-Malaise, \"weird\"/E-ADR/E-Malaise, \",\"/O/O, \"didnt\"/O/O, \"have\"/O/O, \"that\"/O/O, \"when\"/O/O, \"on\"/O/O, \"50\"/O/O, \".\"/O/O],\n",
       " Sentence[39]: \"hunger pangs . brilliant , i have a new lease of life , i walk up & down steps properly , no longer sideways like a toddler , hip pain as gone other than if i jar it .\" → [\"hunger\"/B-ADR/B-Hunger_pain, \"pangs\"/E-ADR/E-Hunger_pain, \".\"/O/O, \"brilliant\"/O/O, \",\"/O/O, \"i\"/O/O, \"have\"/O/O, \"a\"/O/O, \"new\"/O/O, \"lease\"/O/O, \"of\"/O/O, \"life\"/O/O, \",\"/O/O, \"i\"/O/O, \"walk\"/B-Symptom/B-CONCEPT_LESS, \"up\"/I-Symptom/I-CONCEPT_LESS, \"&\"/I-Symptom/I-CONCEPT_LESS, \"down\"/I-Symptom/I-CONCEPT_LESS, \"steps\"/I-Symptom/I-CONCEPT_LESS, \"properly\"/O/O, \",\"/O/O, \"no\"/O/O, \"longer\"/O/O, \"sideways\"/E-Symptom/E-CONCEPT_LESS, \"like\"/O/O, \"a\"/O/O, \"toddler\"/O/O, \",\"/O/O, \"hip\"/B-Symptom/B-Hip_pain, \"pain\"/E-Symptom/E-Hip_pain, \"as\"/O/O, \"gone\"/O/O, \"other\"/O/O, \"than\"/O/O, \"if\"/O/O, \"i\"/O/O, \"jar\"/O/O, \"it\"/O/O, \".\"/O/O],\n",
       " Sentence[51]: \"no side effects for the first two months . then vaginal bleeding 2 wks after menstral cycle . stomach pain . canker sores in my mouth . headache . been off for 1 week still have bleeding . helped my pain alot . too scared to take this drug again .\" → [\"no\"/O/O, \"side\"/O/O, \"effects\"/O/O, \"for\"/O/O, \"the\"/O/O, \"first\"/O/O, \"two\"/O/O, \"months\"/O/O, \".\"/O/O, \"then\"/O/O, \"vaginal\"/B-ADR/B-Vaginal_bleeding, \"bleeding\"/E-ADR/E-Vaginal_bleeding, \"2\"/O/O, \"wks\"/O/O, \"after\"/O/O, \"menstral\"/O/O, \"cycle\"/O/O, \".\"/O/O, \"stomach\"/B-ADR/B-Stomach_ache, \"pain\"/E-ADR/E-Stomach_ache, \".\"/O/O, \"canker\"/B-ADR/B-Aphthous_ulcer, \"sores\"/E-ADR/E-Aphthous_ulcer, \"in\"/O/O, \"my\"/O/O, \"mouth\"/O/O, \".\"/O/O, \"headache\"/S-ADR/S-Headache, \".\"/O/O, \"been\"/O/O, \"off\"/O/O, \"for\"/O/O, \"1\"/O/O, \"week\"/O/O, \"still\"/O/O, \"have\"/O/O, \"bleeding\"/O/O, \".\"/O/O, \"helped\"/O/O, \"my\"/O/O, \"pain\"/S-Symptom/S-Pain, \"alot\"/O/O, \".\"/O/O, \"too\"/O/O, \"scared\"/O/O, \"to\"/O/O, \"take\"/O/O, \"this\"/O/O, \"drug\"/O/O, \"again\"/O/O, \".\"/O/O],\n",
       " Sentence[68]: \"1st pill taken with food , a few hours after i experienced shortness of breath , a sense of depression , cramping , upset stomache will stop taking immediately . honestly can not recommend this drug , i am still experiencing side effects just after 1 pill and im supposed to take twice / day 75mg . i just cant keep going have to try something else .\" → [\"1st\"/O/O, \"pill\"/O/O, \"taken\"/O/O, \"with\"/O/O, \"food\"/O/O, \",\"/O/O, \"a\"/O/O, \"few\"/O/O, \"hours\"/O/O, \"after\"/O/O, \"i\"/O/O, \"experienced\"/O/O, \"shortness\"/B-ADR/B-Dyspnoea, \"of\"/I-ADR/I-Dyspnoea, \"breath\"/E-ADR/E-Dyspnoea, \",\"/O/O, \"a\"/O/O, \"sense\"/O/O, \"of\"/O/O, \"depression\"/S-ADR/S-Depression, \",\"/O/O, \"cramping\"/S-ADR/S-Muscle_cramp, \",\"/O/O, \"upset\"/B-ADR/B-Upset_stomach, \"stomache\"/E-ADR/E-Upset_stomach, \"will\"/O/O, \"stop\"/O/O, \"taking\"/O/O, \"immediately\"/O/O, \".\"/O/O, \"honestly\"/O/O, \"can\"/O/O, \"not\"/O/O, \"recommend\"/O/O, \"this\"/O/O, \"drug\"/O/O, \",\"/O/O, \"i\"/O/O, \"am\"/O/O, \"still\"/O/O, \"experiencing\"/O/O, \"side\"/O/O, \"effects\"/O/O, \"just\"/O/O, \"after\"/O/O, \"1\"/O/O, \"pill\"/O/O, \"and\"/O/O, \"im\"/O/O, \"supposed\"/O/O, \"to\"/O/O, \"take\"/O/O, \"twice\"/O/O, \"/\"/O/O, \"day\"/O/O, \"75mg\"/O/O, \".\"/O/O, \"i\"/O/O, \"just\"/O/O, \"cant\"/O/O, \"keep\"/O/O, \"going\"/O/O, \"have\"/O/O, \"to\"/O/O, \"try\"/O/O, \"something\"/O/O, \"else\"/O/O, \".\"/O/O],\n",
       " Sentence[50]: \"i have had no side effects been taking arthrotec a little over a year , have not noticed any side effects . it does help alot i noticed that when there are times when i forget to take it i cant stand or walk for any lengths of time .\" → [\"i\"/O/O, \"have\"/O/O, \"had\"/O/O, \"no\"/O/O, \"side\"/O/O, \"effects\"/O/O, \"been\"/O/O, \"taking\"/O/O, \"arthrotec\"/S-Drug/S-Arthrotec, \"a\"/O/O, \"little\"/O/O, \"over\"/O/O, \"a\"/O/O, \"year\"/O/O, \",\"/O/O, \"have\"/O/O, \"not\"/O/O, \"noticed\"/O/O, \"any\"/O/O, \"side\"/O/O, \"effects\"/O/O, \".\"/O/O, \"it\"/O/O, \"does\"/O/O, \"help\"/O/O, \"alot\"/O/O, \"i\"/O/O, \"noticed\"/O/O, \"that\"/O/O, \"when\"/O/O, \"there\"/O/O, \"are\"/O/O, \"times\"/O/O, \"when\"/O/O, \"i\"/O/O, \"forget\"/O/O, \"to\"/O/O, \"take\"/O/O, \"it\"/O/O, \"i\"/B-Symptom/B-Reduced_mobility, \"cant\"/I-Symptom/I-Reduced_mobility, \"stand\"/I-Symptom/I-Reduced_mobility, \"or\"/I-Symptom/I-Reduced_mobility, \"walk\"/I-Symptom/I-Reduced_mobility, \"for\"/I-Symptom/I-Reduced_mobility, \"any\"/I-Symptom/I-Reduced_mobility, \"lengths\"/I-Symptom/I-Reduced_mobility, \"of\"/I-Symptom/I-Reduced_mobility, \"time\"/E-Symptom/E-Reduced_mobility, \".\"/O/O],\n",
       " Sentence[89]: \"it seems as if bleeding forever is a side effect but doctors tell you that its not the drug . i have been on arthotec for three months i still have pain and stiffness . at first week into this drug i was in so much pain i sat on the floor at work and thought i was gaving a baby . i always feel weak and almost fainted a bunch of times . consider somthing that works i am switching today as this isnt in my head .\" → [\"it\"/O/O, \"seems\"/O/O, \"as\"/O/O, \"if\"/O/O, \"bleeding\"/S-ADR/S-Bleeding, \"forever\"/O/O, \"is\"/O/O, \"a\"/O/O, \"side\"/O/O, \"effect\"/O/O, \"but\"/O/O, \"doctors\"/O/O, \"tell\"/O/O, \"you\"/O/O, \"that\"/O/O, \"its\"/O/O, \"not\"/O/O, \"the\"/O/O, \"drug\"/O/O, \".\"/O/O, \"i\"/O/O, \"have\"/O/O, \"been\"/O/O, \"on\"/O/O, \"arthotec\"/S-Drug/S-Arthrotec, \"for\"/O/O, \"three\"/O/O, \"months\"/O/O, \"i\"/O/O, \"still\"/O/O, \"have\"/O/O, \"pain\"/B-Symptom/B-Pain, \"and\"/I-Symptom/I-Pain, \"stiffness\"/E-Symptom/E-Pain, \".\"/O/O, \"at\"/O/O, \"first\"/O/O, \"week\"/O/O, \"into\"/O/O, \"this\"/O/O, \"drug\"/O/O, \"i\"/O/O, \"was\"/O/O, \"in\"/O/O, \"so\"/B-ADR/B-Pain, \"much\"/I-ADR/I-Pain, \"pain\"/E-ADR/E-Pain, \"i\"/O/O, \"sat\"/O/O, \"on\"/O/O, \"the\"/O/O, \"floor\"/O/O, \"at\"/O/O, \"work\"/O/O, \"and\"/O/O, \"thought\"/O/O, \"i\"/O/O, \"was\"/O/O, \"gaving\"/O/O, \"a\"/O/O, \"baby\"/O/O, \".\"/O/O, \"i\"/O/O, \"always\"/O/O, \"feel\"/B-ADR/B-Malaise_and_fatigue, \"weak\"/I-ADR/I-Malaise_and_fatigue, \"and\"/I-ADR/I-Malaise_and_fatigue, \"almost\"/I-ADR/I-Malaise_and_fatigue, \"fainted\"/E-ADR/E-Malaise_and_fatigue, \"a\"/O/O, \"bunch\"/O/O, \"of\"/O/O, \"times\"/O/O, \".\"/O/O, \"consider\"/O/O, \"somthing\"/O/O, \"that\"/O/O, \"works\"/O/O, \"i\"/O/O, \"am\"/O/O, \"switching\"/O/O, \"today\"/O/O, \"as\"/O/O, \"this\"/O/O, \"isnt\"/O/O, \"in\"/O/O, \"my\"/O/O, \"head\"/O/O, \".\"/O/O],\n",
       " Sentence[56]: \"some diarrhea , some constipation , fatigue . it works very well for the heel ; i couldnt walk on it very well before taking it . i take 75mg . twice a day . also have severe osteoarthritis in the knees and hands , but it doesnt seem to relieve that pain at all .\" → [\"some\"/O/O, \"diarrhea\"/S-ADR/S-Diarrhoea, \",\"/O/O, \"some\"/O/O, \"constipation\"/S-ADR/S-Constipation, \",\"/O/O, \"fatigue\"/S-ADR/S-Fatigue, \".\"/O/O, \"it\"/O/O, \"works\"/O/O, \"very\"/O/O, \"well\"/O/O, \"for\"/O/O, \"the\"/B-Symptom/B-Walking_disability, \"heel\"/I-Symptom/I-Walking_disability, \";\"/O/O, \"i\"/I-Symptom/I-Walking_disability, \"couldnt\"/I-Symptom/I-Walking_disability, \"walk\"/I-Symptom/I-Walking_disability, \"on\"/I-Symptom/I-Walking_disability, \"it\"/O/O, \"very\"/I-Symptom/I-Walking_disability, \"well\"/E-Symptom/E-Walking_disability, \"before\"/O/O, \"taking\"/O/O, \"it\"/O/O, \".\"/O/O, \"i\"/O/O, \"take\"/O/O, \"75mg\"/O/O, \".\"/O/O, \"twice\"/O/O, \"a\"/O/O, \"day\"/O/O, \".\"/O/O, \"also\"/O/O, \"have\"/O/O, \"severe\"/B-Disease/B-Osteoarthritis_of_knee/B-Osteoarthrosis_of_hand, \"osteoarthritis\"/I-Disease/I-Osteoarthritis_of_knee/I-Osteoarthrosis_of_hand, \"in\"/I-Disease/I-Osteoarthritis_of_knee/I-Osteoarthrosis_of_hand, \"the\"/I-Disease/I-Osteoarthritis_of_knee/I-Osteoarthrosis_of_hand, \"knees\"/E-Disease/E-Osteoarthritis_of_knee, \"and\"/O/O, \"hands\"/E-Disease/E-Osteoarthrosis_of_hand, \",\"/O/O, \"but\"/O/O, \"it\"/O/O, \"doesnt\"/O/O, \"seem\"/O/O, \"to\"/O/O, \"relieve\"/O/O, \"that\"/O/O, \"pain\"/S-Symptom/S-Pain, \"at\"/O/O, \"all\"/O/O, \".\"/O/O],\n",
       " Sentence[84]: \"ive been on 75mg twice daily for 13 days , with recent body swelling , face , wrists , abdomen , thighs - clothes fit tight . insomina is much worse . i have forgetfulnes and confussion - really bad - almost dangerous . have felt \" empty stomach \" feeling , but not bothersome . have had more loose stools , but tolerable . im looking for a more natural therapy , checking into ha ( hyaluronic acid ) to replace arthrotec .\" → [\"ive\"/O/O, \"been\"/O/O, \"on\"/O/O, \"75mg\"/O/O, \"twice\"/O/O, \"daily\"/O/O, \"for\"/O/O, \"13\"/O/O, \"days\"/O/O, \",\"/O/O, \"with\"/O/O, \"recent\"/O/O, \"body\"/B-ADR/B-Swelling_of_body_region, \"swelling\"/B-ADR/E-ADR/B-Abdominal_swelling/B-Facial_swelling/E-Swelling_of_body_region/B-Swelling_of_lower_limb/B-Swelling_of_wrist_joint, \",\"/O/O, \"face\"/E-ADR/E-Facial_swelling, \",\"/O/O, \"wrists\"/E-ADR/E-Swelling_of_wrist_joint, \",\"/O/O, \"abdomen\"/E-ADR/E-Abdominal_swelling, \",\"/O/O, \"thighs\"/E-ADR/E-Swelling_of_lower_limb, \"-\"/O/O, \"clothes\"/O/O, \"fit\"/O/O, \"tight\"/O/O, \".\"/O/O, \"insomina\"/S-ADR/S-Insomnia, \"is\"/O/O, \"much\"/O/O, \"worse\"/O/O, \".\"/O/O, \"i\"/O/O, \"have\"/O/O, \"forgetfulnes\"/B-ADR/B-Forgetful, \"and\"/I-ADR/I-Forgetful, \"confussion\"/E-ADR/E-Forgetful, \"-\"/O/O, \"really\"/O/O, \"bad\"/O/O, \"-\"/O/O, \"almost\"/O/O, \"dangerous\"/O/O, \".\"/O/O, \"have\"/O/O, \"felt\"/O/O, \"\"\"/B-ADR/B-Feeling_empty, \"empty\"/I-ADR/I-Feeling_empty, \"stomach\"/I-ADR/I-Feeling_empty, \"\"\"/I-ADR/I-Feeling_empty, \"feeling\"/E-ADR/E-Feeling_empty, \",\"/O/O, \"but\"/O/O, \"not\"/O/O, \"bothersome\"/O/O, \".\"/O/O, \"have\"/O/O, \"had\"/O/O, \"more\"/O/O, \"loose\"/B-ADR/B-Loose_stool, \"stools\"/E-ADR/E-Loose_stool, \",\"/O/O, \"but\"/O/O, \"tolerable\"/O/O, \".\"/O/O, \"im\"/O/O, \"looking\"/O/O, \"for\"/O/O, \"a\"/O/O, \"more\"/O/O, \"natural\"/O/O, \"therapy\"/O/O, \",\"/O/O, \"checking\"/O/O, \"into\"/O/O, \"ha\"/B-Drug/B-CONCEPT_LESS, \"(\"/I-Drug/I-CONCEPT_LESS, \"hyaluronic\"/I-Drug/I-CONCEPT_LESS, \"acid\"/I-Drug/I-CONCEPT_LESS, \")\"/E-Drug/E-CONCEPT_LESS, \"to\"/O/O, \"replace\"/O/O, \"arthrotec\"/S-Drug/S-Arthrotec, \".\"/O/O],\n",
       " Sentence[68]: \"none that i can think of . i recently had bilateral knee replacement . prior to that , arthrotec was the only drug that helped . i tried every drug on the market , but , to no avail . i took 50 mg . 3 times a day of arthrotec and was almost pain free . i highly recommend it to anyone that suffers from arthritis .\" → [\"none\"/O/O, \"that\"/O/O, \"i\"/O/O, \"can\"/O/O, \"think\"/O/O, \"of\"/O/O, \".\"/O/O, \"i\"/O/O, \"recently\"/O/O, \"had\"/O/O, \"bilateral\"/O/O, \"knee\"/O/O, \"replacement\"/O/O, \".\"/O/O, \"prior\"/O/O, \"to\"/O/O, \"that\"/O/O, \",\"/O/O, \"arthrotec\"/S-Drug/S-Arthrotec, \"was\"/O/O, \"the\"/O/O, \"only\"/O/O, \"drug\"/O/O, \"that\"/O/O, \"helped\"/O/O, \".\"/O/O, \"i\"/O/O, \"tried\"/O/O, \"every\"/O/O, \"drug\"/O/O, \"on\"/O/O, \"the\"/O/O, \"market\"/O/O, \",\"/O/O, \"but\"/O/O, \",\"/O/O, \"to\"/O/O, \"no\"/O/O, \"avail\"/O/O, \".\"/O/O, \"i\"/O/O, \"took\"/O/O, \"50\"/O/O, \"mg\"/O/O, \".\"/O/O, \"3\"/O/O, \"times\"/O/O, \"a\"/O/O, \"day\"/O/O, \"of\"/O/O, \"arthrotec\"/S-Drug/S-Arthrotec, \"and\"/O/O, \"was\"/O/O, \"almost\"/O/O, \"pain\"/S-Symptom/S-Pain, \"free\"/O/O, \".\"/O/O, \"i\"/O/O, \"highly\"/O/O, \"recommend\"/O/O, \"it\"/O/O, \"to\"/O/O, \"anyone\"/O/O, \"that\"/O/O, \"suffers\"/O/O, \"from\"/O/O, \"arthritis\"/S-Finding/S-Arthritis, \".\"/O/O],\n",
       " Sentence[28]: \"hospitalization due extreme rectal bleed that required blood transfusion . i was extremely sick and initially felt i might not survive . should be removed from market .\" → [\"hospitalization\"/O/O, \"due\"/O/O, \"extreme\"/O/O, \"rectal\"/B-ADR/B-Rectal_haemorrhage, \"bleed\"/E-ADR/E-Rectal_haemorrhage, \"that\"/O/O, \"required\"/O/O, \"blood\"/O/O, \"transfusion\"/O/O, \".\"/O/O, \"i\"/O/O, \"was\"/O/O, \"extremely\"/B-ADR/B-Generally_unwell, \"sick\"/E-ADR/E-Generally_unwell, \"and\"/O/O, \"initially\"/O/O, \"felt\"/B-ADR/B-CONCEPT_LESS, \"i\"/I-ADR/I-CONCEPT_LESS, \"might\"/I-ADR/I-CONCEPT_LESS, \"not\"/I-ADR/I-CONCEPT_LESS, \"survive\"/E-ADR/E-CONCEPT_LESS, \".\"/O/O, \"should\"/O/O, \"be\"/O/O, \"removed\"/O/O, \"from\"/O/O, \"market\"/O/O, \".\"/O/O]]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(data_save_dir+'/cadec_flair_objects.pkl', 'rb') as f:\n",
    "    docs = pickle.load(f)\n",
    "docs[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1186"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Sentence.to_plain_string of Sentence[157]: \"wow , so im not crazy after all ! my dr cant seem to find out why my feet hurt in the morning and unexplained joint pain , i get even after just sitting for an hour watching tv in the evening . man i get up and i can hardly walk . stiffness in my feet , and leg joint . dr has done x - ray and everything . im think he is thinking it is in my head all this pain . now when i so called sleep at night i have been noticing i get numbness in my hands , and it isnt due to me lying on my arms or anything , and my lt wrist joint , where my thumb is attached , is dislocating or it painfully sticks . and i have to painfully put it back in . im going to show this website to my gp ! .\" → [\"wow\"/O/O, \",\"/O/O, \"so\"/O/O, \"im\"/O/O, \"not\"/O/O, \"crazy\"/O/O, \"after\"/O/O, \"all\"/O/O, \"!\"/O/O, \"my\"/O/O, \"dr\"/O/O, \"cant\"/O/O, \"seem\"/O/O, \"to\"/O/O, \"find\"/O/O, \"out\"/O/O, \"why\"/O/O, \"my\"/O/O, \"feet\"/B-ADR/B-Foot_pain, \"hurt\"/E-ADR/E-Foot_pain, \"in\"/O/O, \"the\"/O/O, \"morning\"/O/O, \"and\"/O/O, \"unexplained\"/O/O, \"joint\"/B-ADR/B-Arthralgia, \"pain\"/E-ADR/E-Arthralgia, \",\"/O/O, \"i\"/O/O, \"get\"/O/O, \"even\"/O/O, \"after\"/O/O, \"just\"/O/O, \"sitting\"/O/O, \"for\"/O/O, \"an\"/O/O, \"hour\"/O/O, \"watching\"/O/O, \"tv\"/O/O, \"in\"/O/O, \"the\"/O/O, \"evening\"/O/O, \".\"/O/O, \"man\"/O/O, \"i\"/O/O, \"get\"/O/O, \"up\"/O/O, \"and\"/O/O, \"i\"/O/O, \"can\"/B-ADR/B-Walking_disability, \"hardly\"/I-ADR/I-Walking_disability, \"walk\"/E-ADR/E-Walking_disability, \".\"/O/O, \"stiffness\"/B-ADR/B-Stiff_legs, \"in\"/I-ADR/I-Stiff_legs, \"my\"/I-ADR/I-Stiff_legs, \"feet\"/E-ADR/E-Stiff_legs, \",\"/O/O, \"and\"/O/O, \"leg\"/I-ADR/I-Stiff_legs, \"joint\"/E-ADR/E-Stiff_legs, \".\"/O/O, \"dr\"/O/O, \"has\"/O/O, \"done\"/O/O, \"x\"/O/O, \"-\"/O/O, \"ray\"/O/O, \"and\"/O/O, \"everything\"/O/O, \".\"/O/O, \"im\"/O/O, \"think\"/O/O, \"he\"/O/O, \"is\"/O/O, \"thinking\"/O/O, \"it\"/O/O, \"is\"/O/O, \"in\"/O/O, \"my\"/O/O, \"head\"/O/O, \"all\"/O/O, \"this\"/O/O, \"pain\"/S-ADR/S-Pain, \".\"/O/O, \"now\"/O/O, \"when\"/O/O, \"i\"/O/O, \"so\"/O/O, \"called\"/O/O, \"sleep\"/O/O, \"at\"/O/O, \"night\"/O/O, \"i\"/O/O, \"have\"/O/O, \"been\"/O/O, \"noticing\"/O/O, \"i\"/O/O, \"get\"/O/O, \"numbness\"/B-ADR/B-Numbness_of_hand, \"in\"/I-ADR/I-Numbness_of_hand, \"my\"/I-ADR/I-Numbness_of_hand, \"hands\"/E-ADR/E-Numbness_of_hand, \",\"/O/O, \"and\"/O/O, \"it\"/O/O, \"isnt\"/O/O, \"due\"/O/O, \"to\"/O/O, \"me\"/O/O, \"lying\"/O/O, \"on\"/O/O, \"my\"/O/O, \"arms\"/O/O, \"or\"/O/O, \"anything\"/O/O, \",\"/O/O, \"and\"/O/O, \"my\"/O/O, \"lt\"/B-ADR/B-CONCEPT_LESS, \"wrist\"/I-ADR/I-CONCEPT_LESS, \"joint\"/E-ADR/E-CONCEPT_LESS, \",\"/O/O, \"where\"/O/O, \"my\"/O/O, \"thumb\"/O/O, \"is\"/O/O, \"attached\"/O/O, \",\"/O/O, \"is\"/O/O, \"dislocating\"/S-ADR/S-Dislocation_of_joint, \"or\"/O/O, \"it\"/O/O, \"painfully\"/O/O, \"sticks\"/O/O, \".\"/O/O, \"and\"/O/O, \"i\"/O/O, \"have\"/O/O, \"to\"/O/O, \"painfully\"/O/O, \"put\"/O/O, \"it\"/O/O, \"back\"/O/O, \"in\"/O/O, \".\"/O/O, \"im\"/O/O, \"going\"/O/O, \"to\"/O/O, \"show\"/O/O, \"this\"/O/O, \"website\"/O/O, \"to\"/O/O, \"my\"/O/O, \"gp\"/O/O, \"!\"/O/O, \".\"/O/O]>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = docs[433]\n",
    "temp.to_plain_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ner': ['Token[0]: \"wow\"'/'O' (1.0),\n",
       "  'Token[1]: \",\"'/'O' (1.0),\n",
       "  'Token[2]: \"so\"'/'O' (1.0),\n",
       "  'Token[3]: \"im\"'/'O' (1.0),\n",
       "  'Token[4]: \"not\"'/'O' (1.0),\n",
       "  'Token[5]: \"crazy\"'/'O' (1.0),\n",
       "  'Token[6]: \"after\"'/'O' (1.0),\n",
       "  'Token[7]: \"all\"'/'O' (1.0),\n",
       "  'Token[8]: \"!\"'/'O' (1.0),\n",
       "  'Token[9]: \"my\"'/'O' (1.0),\n",
       "  'Token[10]: \"dr\"'/'O' (1.0),\n",
       "  'Token[11]: \"cant\"'/'O' (1.0),\n",
       "  'Token[12]: \"seem\"'/'O' (1.0),\n",
       "  'Token[13]: \"to\"'/'O' (1.0),\n",
       "  'Token[14]: \"find\"'/'O' (1.0),\n",
       "  'Token[15]: \"out\"'/'O' (1.0),\n",
       "  'Token[16]: \"why\"'/'O' (1.0),\n",
       "  'Token[17]: \"my\"'/'O' (1.0),\n",
       "  'Token[18]: \"feet\"'/'B-ADR' (1.0),\n",
       "  'Token[19]: \"hurt\"'/'E-ADR' (1.0),\n",
       "  'Token[20]: \"in\"'/'O' (1.0),\n",
       "  'Token[21]: \"the\"'/'O' (1.0),\n",
       "  'Token[22]: \"morning\"'/'O' (1.0),\n",
       "  'Token[23]: \"and\"'/'O' (1.0),\n",
       "  'Token[24]: \"unexplained\"'/'O' (1.0),\n",
       "  'Token[25]: \"joint\"'/'B-ADR' (1.0),\n",
       "  'Token[26]: \"pain\"'/'E-ADR' (1.0),\n",
       "  'Token[27]: \",\"'/'O' (1.0),\n",
       "  'Token[28]: \"i\"'/'O' (1.0),\n",
       "  'Token[29]: \"get\"'/'O' (1.0),\n",
       "  'Token[30]: \"even\"'/'O' (1.0),\n",
       "  'Token[31]: \"after\"'/'O' (1.0),\n",
       "  'Token[32]: \"just\"'/'O' (1.0),\n",
       "  'Token[33]: \"sitting\"'/'O' (1.0),\n",
       "  'Token[34]: \"for\"'/'O' (1.0),\n",
       "  'Token[35]: \"an\"'/'O' (1.0),\n",
       "  'Token[36]: \"hour\"'/'O' (1.0),\n",
       "  'Token[37]: \"watching\"'/'O' (1.0),\n",
       "  'Token[38]: \"tv\"'/'O' (1.0),\n",
       "  'Token[39]: \"in\"'/'O' (1.0),\n",
       "  'Token[40]: \"the\"'/'O' (1.0),\n",
       "  'Token[41]: \"evening\"'/'O' (1.0),\n",
       "  'Token[42]: \".\"'/'O' (1.0),\n",
       "  'Token[43]: \"man\"'/'O' (1.0),\n",
       "  'Token[44]: \"i\"'/'O' (1.0),\n",
       "  'Token[45]: \"get\"'/'O' (1.0),\n",
       "  'Token[46]: \"up\"'/'O' (1.0),\n",
       "  'Token[47]: \"and\"'/'O' (1.0),\n",
       "  'Token[48]: \"i\"'/'O' (1.0),\n",
       "  'Token[49]: \"can\"'/'B-ADR' (1.0),\n",
       "  'Token[50]: \"hardly\"'/'I-ADR' (1.0),\n",
       "  'Token[51]: \"walk\"'/'E-ADR' (1.0),\n",
       "  'Token[52]: \".\"'/'O' (1.0),\n",
       "  'Token[53]: \"stiffness\"'/'B-ADR' (1.0),\n",
       "  'Token[54]: \"in\"'/'I-ADR' (1.0),\n",
       "  'Token[55]: \"my\"'/'I-ADR' (1.0),\n",
       "  'Token[56]: \"feet\"'/'E-ADR' (1.0),\n",
       "  'Token[57]: \",\"'/'O' (1.0),\n",
       "  'Token[58]: \"and\"'/'O' (1.0),\n",
       "  'Token[59]: \"leg\"'/'I-ADR' (1.0),\n",
       "  'Token[60]: \"joint\"'/'E-ADR' (1.0),\n",
       "  'Token[61]: \".\"'/'O' (1.0),\n",
       "  'Token[62]: \"dr\"'/'O' (1.0),\n",
       "  'Token[63]: \"has\"'/'O' (1.0),\n",
       "  'Token[64]: \"done\"'/'O' (1.0),\n",
       "  'Token[65]: \"x\"'/'O' (1.0),\n",
       "  'Token[66]: \"-\"'/'O' (1.0),\n",
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       "  'Token[68]: \"and\"'/'O' (1.0),\n",
       "  'Token[69]: \"everything\"'/'O' (1.0),\n",
       "  'Token[70]: \".\"'/'O' (1.0),\n",
       "  'Token[71]: \"im\"'/'O' (1.0),\n",
       "  'Token[72]: \"think\"'/'O' (1.0),\n",
       "  'Token[73]: \"he\"'/'O' (1.0),\n",
       "  'Token[74]: \"is\"'/'O' (1.0),\n",
       "  'Token[75]: \"thinking\"'/'O' (1.0),\n",
       "  'Token[76]: \"it\"'/'O' (1.0),\n",
       "  'Token[77]: \"is\"'/'O' (1.0),\n",
       "  'Token[78]: \"in\"'/'O' (1.0),\n",
       "  'Token[79]: \"my\"'/'O' (1.0),\n",
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       "  'Token[81]: \"all\"'/'O' (1.0),\n",
       "  'Token[82]: \"this\"'/'O' (1.0),\n",
       "  'Token[83]: \"pain\"'/'S-ADR' (1.0),\n",
       "  'Token[84]: \".\"'/'O' (1.0),\n",
       "  'Token[85]: \"now\"'/'O' (1.0),\n",
       "  'Token[86]: \"when\"'/'O' (1.0),\n",
       "  'Token[87]: \"i\"'/'O' (1.0),\n",
       "  'Token[88]: \"so\"'/'O' (1.0),\n",
       "  'Token[89]: \"called\"'/'O' (1.0),\n",
       "  'Token[90]: \"sleep\"'/'O' (1.0),\n",
       "  'Token[91]: \"at\"'/'O' (1.0),\n",
       "  'Token[92]: \"night\"'/'O' (1.0),\n",
       "  'Token[93]: \"i\"'/'O' (1.0),\n",
       "  'Token[94]: \"have\"'/'O' (1.0),\n",
       "  'Token[95]: \"been\"'/'O' (1.0),\n",
       "  'Token[96]: \"noticing\"'/'O' (1.0),\n",
       "  'Token[97]: \"i\"'/'O' (1.0),\n",
       "  'Token[98]: \"get\"'/'O' (1.0),\n",
       "  'Token[99]: \"numbness\"'/'B-ADR' (1.0),\n",
       "  'Token[100]: \"in\"'/'I-ADR' (1.0),\n",
       "  'Token[101]: \"my\"'/'I-ADR' (1.0),\n",
       "  'Token[102]: \"hands\"'/'E-ADR' (1.0),\n",
       "  'Token[103]: \",\"'/'O' (1.0),\n",
       "  'Token[104]: \"and\"'/'O' (1.0),\n",
       "  'Token[105]: \"it\"'/'O' (1.0),\n",
       "  'Token[106]: \"isnt\"'/'O' (1.0),\n",
       "  'Token[107]: \"due\"'/'O' (1.0),\n",
       "  'Token[108]: \"to\"'/'O' (1.0),\n",
       "  'Token[109]: \"me\"'/'O' (1.0),\n",
       "  'Token[110]: \"lying\"'/'O' (1.0),\n",
       "  'Token[111]: \"on\"'/'O' (1.0),\n",
       "  'Token[112]: \"my\"'/'O' (1.0),\n",
       "  'Token[113]: \"arms\"'/'O' (1.0),\n",
       "  'Token[114]: \"or\"'/'O' (1.0),\n",
       "  'Token[115]: \"anything\"'/'O' (1.0),\n",
       "  'Token[116]: \",\"'/'O' (1.0),\n",
       "  'Token[117]: \"and\"'/'O' (1.0),\n",
       "  'Token[118]: \"my\"'/'O' (1.0),\n",
       "  'Token[119]: \"lt\"'/'B-ADR' (1.0),\n",
       "  'Token[120]: \"wrist\"'/'I-ADR' (1.0),\n",
       "  'Token[121]: \"joint\"'/'E-ADR' (1.0),\n",
       "  'Token[122]: \",\"'/'O' (1.0),\n",
       "  'Token[123]: \"where\"'/'O' (1.0),\n",
       "  'Token[124]: \"my\"'/'O' (1.0),\n",
       "  'Token[125]: \"thumb\"'/'O' (1.0),\n",
       "  'Token[126]: \"is\"'/'O' (1.0),\n",
       "  'Token[127]: \"attached\"'/'O' (1.0),\n",
       "  'Token[128]: \",\"'/'O' (1.0),\n",
       "  'Token[129]: \"is\"'/'O' (1.0),\n",
       "  'Token[130]: \"dislocating\"'/'S-ADR' (1.0),\n",
       "  'Token[131]: \"or\"'/'O' (1.0),\n",
       "  'Token[132]: \"it\"'/'O' (1.0),\n",
       "  'Token[133]: \"painfully\"'/'O' (1.0),\n",
       "  'Token[134]: \"sticks\"'/'O' (1.0),\n",
       "  'Token[135]: \".\"'/'O' (1.0),\n",
       "  'Token[136]: \"and\"'/'O' (1.0),\n",
       "  'Token[137]: \"i\"'/'O' (1.0),\n",
       "  'Token[138]: \"have\"'/'O' (1.0),\n",
       "  'Token[139]: \"to\"'/'O' (1.0),\n",
       "  'Token[140]: \"painfully\"'/'O' (1.0),\n",
       "  'Token[141]: \"put\"'/'O' (1.0),\n",
       "  'Token[142]: \"it\"'/'O' (1.0),\n",
       "  'Token[143]: \"back\"'/'O' (1.0),\n",
       "  'Token[144]: \"in\"'/'O' (1.0),\n",
       "  'Token[145]: \".\"'/'O' (1.0),\n",
       "  'Token[146]: \"im\"'/'O' (1.0),\n",
       "  'Token[147]: \"going\"'/'O' (1.0),\n",
       "  'Token[148]: \"to\"'/'O' (1.0),\n",
       "  'Token[149]: \"show\"'/'O' (1.0),\n",
       "  'Token[150]: \"this\"'/'O' (1.0),\n",
       "  'Token[151]: \"website\"'/'O' (1.0),\n",
       "  'Token[152]: \"to\"'/'O' (1.0),\n",
       "  'Token[153]: \"my\"'/'O' (1.0),\n",
       "  'Token[154]: \"gp\"'/'O' (1.0),\n",
       "  'Token[155]: \"!\"'/'O' (1.0),\n",
       "  'Token[156]: \".\"'/'O' (1.0)],\n",
       " 'meddra': ['Token[0]: \"wow\"'/'O' (1.0),\n",
       "  'Token[1]: \",\"'/'O' (1.0),\n",
       "  'Token[2]: \"so\"'/'O' (1.0),\n",
       "  'Token[3]: \"im\"'/'O' (1.0),\n",
       "  'Token[4]: \"not\"'/'O' (1.0),\n",
       "  'Token[5]: \"crazy\"'/'O' (1.0),\n",
       "  'Token[6]: \"after\"'/'O' (1.0),\n",
       "  'Token[7]: \"all\"'/'O' (1.0),\n",
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       "  'Token[9]: \"my\"'/'O' (1.0),\n",
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       "  'Token[11]: \"cant\"'/'O' (1.0),\n",
       "  'Token[12]: \"seem\"'/'O' (1.0),\n",
       "  'Token[13]: \"to\"'/'O' (1.0),\n",
       "  'Token[14]: \"find\"'/'O' (1.0),\n",
       "  'Token[15]: \"out\"'/'O' (1.0),\n",
       "  'Token[16]: \"why\"'/'O' (1.0),\n",
       "  'Token[17]: \"my\"'/'O' (1.0),\n",
       "  'Token[18]: \"feet\"'/'B-Foot_pain' (1.0),\n",
       "  'Token[19]: \"hurt\"'/'E-Foot_pain' (1.0),\n",
       "  'Token[20]: \"in\"'/'O' (1.0),\n",
       "  'Token[21]: \"the\"'/'O' (1.0),\n",
       "  'Token[22]: \"morning\"'/'O' (1.0),\n",
       "  'Token[23]: \"and\"'/'O' (1.0),\n",
       "  'Token[24]: \"unexplained\"'/'O' (1.0),\n",
       "  'Token[25]: \"joint\"'/'B-Arthralgia' (1.0),\n",
       "  'Token[26]: \"pain\"'/'E-Arthralgia' (1.0),\n",
       "  'Token[27]: \",\"'/'O' (1.0),\n",
       "  'Token[28]: \"i\"'/'O' (1.0),\n",
       "  'Token[29]: \"get\"'/'O' (1.0),\n",
       "  'Token[30]: \"even\"'/'O' (1.0),\n",
       "  'Token[31]: \"after\"'/'O' (1.0),\n",
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       "  'Token[34]: \"for\"'/'O' (1.0),\n",
       "  'Token[35]: \"an\"'/'O' (1.0),\n",
       "  'Token[36]: \"hour\"'/'O' (1.0),\n",
       "  'Token[37]: \"watching\"'/'O' (1.0),\n",
       "  'Token[38]: \"tv\"'/'O' (1.0),\n",
       "  'Token[39]: \"in\"'/'O' (1.0),\n",
       "  'Token[40]: \"the\"'/'O' (1.0),\n",
       "  'Token[41]: \"evening\"'/'O' (1.0),\n",
       "  'Token[42]: \".\"'/'O' (1.0),\n",
       "  'Token[43]: \"man\"'/'O' (1.0),\n",
       "  'Token[44]: \"i\"'/'O' (1.0),\n",
       "  'Token[45]: \"get\"'/'O' (1.0),\n",
       "  'Token[46]: \"up\"'/'O' (1.0),\n",
       "  'Token[47]: \"and\"'/'O' (1.0),\n",
       "  'Token[48]: \"i\"'/'O' (1.0),\n",
       "  'Token[49]: \"can\"'/'B-Walking_disability' (1.0),\n",
       "  'Token[50]: \"hardly\"'/'I-Walking_disability' (1.0),\n",
       "  'Token[51]: \"walk\"'/'E-Walking_disability' (1.0),\n",
       "  'Token[52]: \".\"'/'O' (1.0),\n",
       "  'Token[53]: \"stiffness\"'/'B-Stiff_legs' (1.0),\n",
       "  'Token[54]: \"in\"'/'I-Stiff_legs' (1.0),\n",
       "  'Token[55]: \"my\"'/'I-Stiff_legs' (1.0),\n",
       "  'Token[56]: \"feet\"'/'E-Stiff_legs' (1.0),\n",
       "  'Token[57]: \",\"'/'O' (1.0),\n",
       "  'Token[58]: \"and\"'/'O' (1.0),\n",
       "  'Token[59]: \"leg\"'/'I-Stiff_legs' (1.0),\n",
       "  'Token[60]: \"joint\"'/'E-Stiff_legs' (1.0),\n",
       "  'Token[61]: \".\"'/'O' (1.0),\n",
       "  'Token[62]: \"dr\"'/'O' (1.0),\n",
       "  'Token[63]: \"has\"'/'O' (1.0),\n",
       "  'Token[64]: \"done\"'/'O' (1.0),\n",
       "  'Token[65]: \"x\"'/'O' (1.0),\n",
       "  'Token[66]: \"-\"'/'O' (1.0),\n",
       "  'Token[67]: \"ray\"'/'O' (1.0),\n",
       "  'Token[68]: \"and\"'/'O' (1.0),\n",
       "  'Token[69]: \"everything\"'/'O' (1.0),\n",
       "  'Token[70]: \".\"'/'O' (1.0),\n",
       "  'Token[71]: \"im\"'/'O' (1.0),\n",
       "  'Token[72]: \"think\"'/'O' (1.0),\n",
       "  'Token[73]: \"he\"'/'O' (1.0),\n",
       "  'Token[74]: \"is\"'/'O' (1.0),\n",
       "  'Token[75]: \"thinking\"'/'O' (1.0),\n",
       "  'Token[76]: \"it\"'/'O' (1.0),\n",
       "  'Token[77]: \"is\"'/'O' (1.0),\n",
       "  'Token[78]: \"in\"'/'O' (1.0),\n",
       "  'Token[79]: \"my\"'/'O' (1.0),\n",
       "  'Token[80]: \"head\"'/'O' (1.0),\n",
       "  'Token[81]: \"all\"'/'O' (1.0),\n",
       "  'Token[82]: \"this\"'/'O' (1.0),\n",
       "  'Token[83]: \"pain\"'/'S-Pain' (1.0),\n",
       "  'Token[84]: \".\"'/'O' (1.0),\n",
       "  'Token[85]: \"now\"'/'O' (1.0),\n",
       "  'Token[86]: \"when\"'/'O' (1.0),\n",
       "  'Token[87]: \"i\"'/'O' (1.0),\n",
       "  'Token[88]: \"so\"'/'O' (1.0),\n",
       "  'Token[89]: \"called\"'/'O' (1.0),\n",
       "  'Token[90]: \"sleep\"'/'O' (1.0),\n",
       "  'Token[91]: \"at\"'/'O' (1.0),\n",
       "  'Token[92]: \"night\"'/'O' (1.0),\n",
       "  'Token[93]: \"i\"'/'O' (1.0),\n",
       "  'Token[94]: \"have\"'/'O' (1.0),\n",
       "  'Token[95]: \"been\"'/'O' (1.0),\n",
       "  'Token[96]: \"noticing\"'/'O' (1.0),\n",
       "  'Token[97]: \"i\"'/'O' (1.0),\n",
       "  'Token[98]: \"get\"'/'O' (1.0),\n",
       "  'Token[99]: \"numbness\"'/'B-Numbness_of_hand' (1.0),\n",
       "  'Token[100]: \"in\"'/'I-Numbness_of_hand' (1.0),\n",
       "  'Token[101]: \"my\"'/'I-Numbness_of_hand' (1.0),\n",
       "  'Token[102]: \"hands\"'/'E-Numbness_of_hand' (1.0),\n",
       "  'Token[103]: \",\"'/'O' (1.0),\n",
       "  'Token[104]: \"and\"'/'O' (1.0),\n",
       "  'Token[105]: \"it\"'/'O' (1.0),\n",
       "  'Token[106]: \"isnt\"'/'O' (1.0),\n",
       "  'Token[107]: \"due\"'/'O' (1.0),\n",
       "  'Token[108]: \"to\"'/'O' (1.0),\n",
       "  'Token[109]: \"me\"'/'O' (1.0),\n",
       "  'Token[110]: \"lying\"'/'O' (1.0),\n",
       "  'Token[111]: \"on\"'/'O' (1.0),\n",
       "  'Token[112]: \"my\"'/'O' (1.0),\n",
       "  'Token[113]: \"arms\"'/'O' (1.0),\n",
       "  'Token[114]: \"or\"'/'O' (1.0),\n",
       "  'Token[115]: \"anything\"'/'O' (1.0),\n",
       "  'Token[116]: \",\"'/'O' (1.0),\n",
       "  'Token[117]: \"and\"'/'O' (1.0),\n",
       "  'Token[118]: \"my\"'/'O' (1.0),\n",
       "  'Token[119]: \"lt\"'/'B-CONCEPT_LESS' (1.0),\n",
       "  'Token[120]: \"wrist\"'/'I-CONCEPT_LESS' (1.0),\n",
       "  'Token[121]: \"joint\"'/'E-CONCEPT_LESS' (1.0),\n",
       "  'Token[122]: \",\"'/'O' (1.0),\n",
       "  'Token[123]: \"where\"'/'O' (1.0),\n",
       "  'Token[124]: \"my\"'/'O' (1.0),\n",
       "  'Token[125]: \"thumb\"'/'O' (1.0),\n",
       "  'Token[126]: \"is\"'/'O' (1.0),\n",
       "  'Token[127]: \"attached\"'/'O' (1.0),\n",
       "  'Token[128]: \",\"'/'O' (1.0),\n",
       "  'Token[129]: \"is\"'/'O' (1.0),\n",
       "  'Token[130]: \"dislocating\"'/'S-Dislocation_of_joint' (1.0),\n",
       "  'Token[131]: \"or\"'/'O' (1.0),\n",
       "  'Token[132]: \"it\"'/'O' (1.0),\n",
       "  'Token[133]: \"painfully\"'/'O' (1.0),\n",
       "  'Token[134]: \"sticks\"'/'O' (1.0),\n",
       "  'Token[135]: \".\"'/'O' (1.0),\n",
       "  'Token[136]: \"and\"'/'O' (1.0),\n",
       "  'Token[137]: \"i\"'/'O' (1.0),\n",
       "  'Token[138]: \"have\"'/'O' (1.0),\n",
       "  'Token[139]: \"to\"'/'O' (1.0),\n",
       "  'Token[140]: \"painfully\"'/'O' (1.0),\n",
       "  'Token[141]: \"put\"'/'O' (1.0),\n",
       "  'Token[142]: \"it\"'/'O' (1.0),\n",
       "  'Token[143]: \"back\"'/'O' (1.0),\n",
       "  'Token[144]: \"in\"'/'O' (1.0),\n",
       "  'Token[145]: \".\"'/'O' (1.0),\n",
       "  'Token[146]: \"im\"'/'O' (1.0),\n",
       "  'Token[147]: \"going\"'/'O' (1.0),\n",
       "  'Token[148]: \"to\"'/'O' (1.0),\n",
       "  'Token[149]: \"show\"'/'O' (1.0),\n",
       "  'Token[150]: \"this\"'/'O' (1.0),\n",
       "  'Token[151]: \"website\"'/'O' (1.0),\n",
       "  'Token[152]: \"to\"'/'O' (1.0),\n",
       "  'Token[153]: \"my\"'/'O' (1.0),\n",
       "  'Token[154]: \"gp\"'/'O' (1.0),\n",
       "  'Token[155]: \"!\"'/'O' (1.0),\n",
       "  'Token[156]: \".\"'/'O' (1.0)]}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.annotation_layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # import seaborn as sns\n",
    "# import matplotlib.pyplot as plt\n",
    "# from collections import Counter\n",
    "# from itertools import chain\n",
    "# # from flair.data import Sentence\n",
    "\n",
    "# Assume that you have a list of Flair Sentence objects called 'sentences'\n",
    "text = ' '.join([str(sentence) for sentence in docs])\n",
    "words = text.split()\n",
    "word_freq = Counter(words)\n",
    "sorted_word_freq = sorted(word_freq.items(), key=lambda x: x[1], reverse=True)\n",
    "freqs = [f for w, f in sorted_word_freq]\n",
    "ranks = range(1, len(sorted_word_freq)+1)\n",
    "\n",
    "# Set the plot style and scale\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"paper\", font_scale=1.5)\n",
    "sns.set_palette(\"Set1\", 8, .75)\n",
    "sns.set(rc={'figure.figsize':(10,10)})\n",
    "sns.color_palette(\"tab10\")\n",
    "\n",
    "# Create the plot\n",
    "sns.lineplot(x=ranks, y=freqs)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.xlabel('Word rank (log scale)')\n",
    "plt.ylabel('Word frequency (log scale)')\n",
    "plt.title('Word Frequency Distribution')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_all_labels(token):\n",
    "    res = dict()\n",
    "    for typename in token.sentence.annotation_layers.keys():\n",
    "        res[typename] = [a for a in token.sentence.annotation_layers[typename] if a.data_point.start_position == token.start_position]\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_docs, test_docs = train_test_split(docs, test_size=0.2, random_state=321)\n",
    "dev_docs, test_docs = train_test_split(test_docs, test_size=0.5, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique NER tags: Counter({'I-ADR': 4449, 'E-ADR': 3506, 'B-ADR': 3349, 'S-ADR': 1363, 'S-Drug': 1286, 'B-Finding': 200, 'E-Finding': 200, 'I-Finding': 149, 'B-Disease': 119, 'E-Disease': 119, 'E-Symptom': 114, 'S-Disease': 112, 'S-Symptom': 112, 'E-Drug': 111, 'B-Symptom': 110, 'B-Drug': 110, 'S-Finding': 109, 'I-Symptom': 104, 'I-Drug': 50, 'I-Disease': 50})\n",
      "Unique Meddra tags: Counter({'S-Lipitor': 825, 'I-CONCEPT_LESS': 514, 'S-Pain': 275, 'I-Severe_pain': 265, 'B-CONCEPT_LESS': 232, 'E-CONCEPT_LESS': 232, 'E-Myalgia': 209, 'B-Myalgia': 205, 'E-Severe_pain': 160, 'B-Severe_pain': 138, 'S-CONCEPT_LESS': 119, 'B-Arthralgia': 107, 'E-Arthralgia': 106, 'I-Muscle_weakness': 93, 'S-Depression': 91, 'I-Myalgia': 87, 'S-Fatigue': 87, 'I-Walking_disability': 75, 'I-Pins_and_needles': 68, 'B-Cramp_in_lower_limb': 67, 'E-Cramp_in_lower_limb': 66, 'I-Poor_short-term_memory': 66, 'I-Weakness_of_limb': 64, 'S-Headache': 63, 'B-Backache': 62, 'E-Backache': 62, 'B-Muscle_weakness': 59, 'E-Muscle_weakness': 59, 'I-Foot_pain': 59, 'B-Pain_in_lower_limb': 58, 'E-Pain_in_lower_limb': 58, 'I-Hip_pain': 58, 'B-Amnesia': 57, 'E-Amnesia': 57, 'I-Pain_in_lower_limb': 57, 'B-Hip_pain': 56, 'E-Hip_pain': 56, 'S-Diarrhoea': 56, 'S-Muscle_cramp': 55, 'I-Shoulder_pain': 54, 'B-Walking_disability': 52, 'E-Walking_disability': 52, 'B-Knee_pain': 50, 'E-Knee_pain': 50, 'I-Cramp_in_lower_limb': 49, 'B-Shoulder_pain': 49, 'E-Shoulder_pain': 49, 'B-Muscle_cramp': 49, 'E-Muscle_cramp': 49, 'E-Stomach_ache': 48, 'S-Excessive_upper_gastrointestinal_gas': 48, 'I-Generalised_aches_and_pains': 48, 'B-Stomach_ache': 47, 'I-Mentally_dull': 47, 'S-Dizziness': 47, 'E-Excessive_upper_gastrointestinal_gas': 46, 'S-Nausea': 45, 'I-Unable_to_get_off_a_bed': 44, 'B-Excessive_upper_gastrointestinal_gas': 44, 'I-Knee_pain': 44, 'E-Neck_pain': 43, 'S-Asthenia': 43, 'B-Foot_pain': 43, 'E-Foot_pain': 43, 'B-Neck_pain': 42, 'I-Neck_pain': 42, 'I-Muscle_atrophy': 41, 'S-Insomnia': 41, 'S-Arthrotec': 41, 'I-Excessive_upper_gastrointestinal_gas': 40, 'S-Arthritis': 40, 'I-Influenza-like_illness': 40, 'I-Backache': 39, 'I-Low_back_pain': 39, 'I-Rash': 38, 'E-Weakness_of_limb': 37, 'S-Zocor': 37, 'I-Myalgia/myositis_-_lower_leg': 37, 'I-Pain_in_calf': 37, 'B-Myocardial_infarction': 36, 'E-Myocardial_infarction': 36, 'I-Unable_to_walk': 36, 'I-Lightheadedness': 36, 'I-Flushing': 36, 'I-Numbness_of_lower_limb': 36, 'E-Pins_and_needles': 35, 'B-Asthenia': 33, 'E-Asthenia': 33, 'E-Muscle_atrophy': 33, 'B-Weakness_of_limb': 33, 'B-Loss_of_hair': 32, 'E-Loss_of_hair': 32, 'I-Arthralgia': 31, 'S-Bloating_symptom': 30, 'I-Pain_in_upper_limb': 30, 'B-Pins_and_needles': 29, 'B-Blurred_vision_-_hazy': 28, 'E-Blurred_vision_-_hazy': 28, 'I-Pain_provoked_by_movement': 28, 'B-Generalised_aches_and_pains': 27, 'E-Generalised_aches_and_pains': 27, 'B-Poor_short-term_memory': 27, 'E-Poor_short-term_memory': 27, 'B-Mentally_dull': 26, 'E-Mentally_dull': 26, 'B-Muscle_atrophy': 26, 'B-Low_back_pain': 26, 'E-Low_back_pain': 26, 'B-Headache': 25, 'E-Headache': 25, 'S-ubidecarenone': 25, 'I-Loss_of_motivation': 25, 'E-Memory_impairment': 25, 'I-Muscle_fatigue': 25, 'B-Fatigue': 25, 'E-Fatigue': 25, 'S-Constipation': 25, 'I-Myalgia/myositis_-_shoulder': 25, 'S-Anxiety': 25, 'I-Stomach_ache': 25, 'I-Thigh_pain': 25, 'I-Bleeding_between_periods': 25, 'B-Memory_impairment': 24, 'I-Reduced_libido': 24, 'B-Chest_pain': 24, 'E-Chest_pain': 24, 'I-Memory_impairment': 23, 'B-Stomach_cramps': 23, 'E-Stomach_cramps': 23, 'I-Sensation_of_hot_and_cold': 23, 'I-Difficulty_walking_up_stairs': 22, 'S-Pravachol': 22, 'S-Voltaren': 22, 'B-Myopathy': 22, 'E-Myopathy': 22, 'I-Generalised_acute_body_pains': 22, 'E-Charleyhorse': 22, 'I-Unable_to_concentrate': 21, 'I-Burning_feet': 21, 'B-Unable_to_walk': 21, 'E-Unable_to_walk': 21, 'B-Influenza-like_illness': 21, 'E-Influenza-like_illness': 21, 'I-Muscle_cramp': 21, 'I-Charleyhorse': 21, 'B-Difficulty_sleeping': 20, 'E-Difficulty_sleeping': 20, 'B-Upset_stomach': 20, 'E-Upset_stomach': 20, 'B-Cannot_sleep_at_all': 20, 'E-Cannot_sleep_at_all': 20, 'I-Ankle_pain': 20, 'I-Rupture_of_Achilles_tendon': 20, 'I-Unable_to_climb_stairs': 20, 'B-Abdominal_pain': 20, 'E-Abdominal_pain': 20, 'I-Unable_to_walk_up_step': 20, 'I-Myalgia/myositis_-_multiple': 20, 'I-Difficulty_sleeping': 19, 'B-Visual_disturbance': 19, 'E-Visual_disturbance': 19, 'B-Pain_in_calf': 19, 'E-Pain_in_calf': 19, 'B-Weight_gain': 19, 'E-Weight_gain': 19, 'I-Thoracic_back_pain': 19, 'I-Visual_disturbance': 19, 'I-Cramp_in_calf': 18, 'I-Headache': 18, 'B-Increased_cholesterol_esters': 18, 'E-Increased_cholesterol_esters': 18, 'B-Heart_disease': 18, 'E-Heart_disease': 18, 'S-Pins_and_needles': 18, 'S-Numbness': 18, 'B-Lack_of_energy': 18, 'E-Lack_of_energy': 18, 'I-Acid_reflux': 18, 'I-Postmenopausal_bleeding': 18, 'I-Paraesthesia_of_lower_extremity': 18, 'E-Muscle_fatigue': 17, 'B-Tired': 17, 'E-Tired': 17, 'B-Reduced_libido': 17, 'E-Reduced_libido': 17, 'I-Tired': 17, 'S-Vertigo': 17, 'I-Serum_cholesterol_raised': 17, 'I-Unable_to_move_arm': 17, 'I-Arthritis_of_hand': 17, 'B-Muscle_fatigue': 16, 'B-Unable_to_concentrate': 16, 'E-Unable_to_concentrate': 16, 'I-Impairment_of_balance': 16, 'I-Restless_legs': 16, 'S-Swelling': 16, 'B-Pain_in_upper_limb': 16, 'E-Pain_in_upper_limb': 16, 'I-Tired_all_the_time': 16, 'E-Myalgia/myositis_-_lower_leg': 16, 'S-Crestor': 16, 'I-Myalgia/myositis_-pelvis/thigh': 16, 'I-Burning_pain': 16, 'S-Tiredness_symptom': 16, 'S-Rash': 15, 'E-Muscle_twitch': 15, 'S-Feeling_irritable': 15, 'I-Intercostal_myalgia': 15, 'I-Failure_to_lose_weight': 15, 'B-Myalgia/myositis_-_lower_leg': 15, 'B-Pain': 15, 'E-Pain': 15, 'I-Pulse_fast': 15, 'B-Dyspnoea': 15, 'I-Dyspnoea': 15, 'E-Dyspnoea': 15, 'S-Itching': 15, 'I-Stiff_legs': 15, 'I-Tight_chest': 15, 'I-Stabbing_pain': 15, 'B-Impairment_of_balance': 14, 'E-Impairment_of_balance': 14, 'I-Cramp_in_foot': 14, 'S-Tired': 14, 'B-Ankle_pain': 14, 'E-Ankle_pain': 14, 'I-Irregular_menstruation': 14, 'S-Diabetes_mellitus': 14, 'S-Fibromyalgia': 14, 'I-Liver_enzymes_abnormal': 14, 'B-Vaginal_bleeding': 14, 'E-Vaginal_bleeding': 14, 'I-Myalgia/myositis_-_forearm': 14, 'I-Unable_to_think_clearly': 14, 'I-Pain': 14, 'S-ezetimibe': 13, 'I-Hypersomnia': 13, 'I-Stomach_cramps': 13, 'I-Finding_of_increased_blood_pressure': 13, 'B-ascorbic_acid': 13, 'E-ascorbic_acid': 13, 'I-Itching_of_skin': 13, 'I-Muscle_tension': 13, 'S-Stiffness': 13, 'I-Pain_provoked_by_walking': 13, 'I-Uterine_spasm': 13, 'I-Difficulty_getting_off_a_bed': 13, 'I-Lack_of_energy': 13, 'I-Limitation_of_joint_movement': 13, 'I-Spasm_of_back_muscles': 13, 'I-Tinnitus': 13, 'I-Upset_stomach': 13, 'I-Urinary_frequency': 13, 'B-Cramp_in_calf': 12, 'E-Cramp_in_calf': 12, 'B-Loss_of_motivation': 12, 'E-Loss_of_motivation': 12, 'I-Numbness_of_limbs': 12, 'B-Muscle_twitch': 12, 'B-Lightheadedness': 12, 'E-Lightheadedness': 12, 'B-Liver_disease': 12, 'E-Liver_disease': 12, 'S-Flatulence/wind': 12, 'B-Acid_reflux': 12, 'E-Acid_reflux': 12, 'S-Exhaustion': 12, 'B-Pain_in_elbow': 12, 'E-Pain_in_elbow': 12, 'I-Generally_unwell': 12, 'I-Myopathy': 12, 'I-Irritable_bowel_syndrome_characterised_by_constipation': 12, 'I-Aphasia': 12, 'I-Tachycardia': 11, 'B-Rash': 11, 'E-Rash': 11, 'B-Mood_swings': 11, 'E-Mood_swings': 11, 'I-Incoordination': 11, 'I-Plantar_fasciitis': 11, 'S-ibuprofen': 11, 'B-Restless_legs': 11, 'E-Restless_legs': 11, 'B-Thigh_pain': 11, 'E-Thigh_pain': 11, 'S-Amyotrophic_lateral_sclerosis': 11, 'S-Bleeding': 11, 'B-Tired_all_the_time': 11, 'E-Tired_all_the_time': 11, 'S-fenofibrate': 11, 'I-Random_blood_sugar_raised': 11, 'I-Sharp_pain': 11, 'E-Peripheral_neuropathy': 11, 'I-Weakness_of_hand': 11, 'B-Charleyhorse': 11, 'I-Pain_in_left_arm': 11, 'I-Deep_pain': 11, 'I-Acute_pain': 11, 'I-Muscle_twitch': 11, 'S-Drowsy': 10, 'S-Tendonitis': 10, 'B-Unable_to_think_clearly': 10, 'E-Unable_to_think_clearly': 10, 'E-Burning_sensation': 10, 'B-Abdominal_colic': 10, 'E-Abdominal_colic': 10, 'E-Cramp_in_foot': 10, 'I-Cannot_sleep_at_all': 10, 'B-Serum_creatinine_raised': 10, 'E-Serum_creatinine_raised': 10, 'I-Menorrhagia': 10, 'B-Aptyalism': 10, 'E-Aptyalism': 10, 'B-Finding_of_increased_blood_pressure': 10, 'E-Finding_of_increased_blood_pressure': 10, 'I-Derealisation': 10, 'E-Constant_pain': 10, 'S-Indigestion': 10, 'I-Blurred_vision_-_hazy': 10, 'I-Numbness_of_upper_limb': 10, 'I-Hand_pain': 10, 'I-Loss_of_taste': 10, 'I-Paraesthesia_of_hand': 10, 'I-Unable_to_balance': 10, 'I-Haematochezia': 10, 'I-Difficulty_chewing': 10, 'I-Burning_sensation': 10, 'S-Vomiting': 10, 'I-Foot_swelling': 10, 'I-Fatigue': 9, 'B-Cramp_in_foot': 9, 'I-Migraine': 9, 'B-Bleeding': 9, 'E-Bleeding': 9, 'E-Pain_provoked_by_movement': 9, 'S-Gout': 9, 'I-Discolouration_of_skin': 9, 'B-Hand_pain': 9, 'E-Hand_pain': 9, 'S-Inflammatory_disorder': 9, 'I-Myalgia/myositis_-_upper_arm': 9, 'I-Loss_of_hair': 9, 'I-Sensation_of_heaviness_in_limbs': 9, 'B-Constant_pain': 9, 'B-Serum_cholesterol_raised': 9, 'E-Serum_cholesterol_raised': 9, 'S-Tinnitus': 9, 'I-Personality_change': 9, 'I-Heavy_legs': 9, 'I-Elbow_joint_pain': 9, 'I-Haemorrhage_of_abdominal_cavity_structure': 9, 'B-Peripheral_neuropathy': 9, 'I-Arthralgia_of_the_lower_leg': 9, 'S-Cerebrovascular_accident': 9, 'S-Celebrex': 9, 'I-Numbness_of_hand': 9, 'I-Calf_muscle_weakness': 9, 'I-Clicking_interphalangeal_joint_of_toe': 9, 'B-Unable_to_get_off_a_bed': 8, 'E-Unable_to_get_off_a_bed': 8, 'B-Hypersomnia': 8, 'E-Hypersomnia': 8, 'B-Burning_sensation': 8, 'S-Burning_sensation': 8, 'B-Plantar_fasciitis': 8, 'E-Plantar_fasciitis': 8, 'B-Stiff_neck': 8, 'E-Stiff_neck': 8, 'S-Lightheadedness': 8, 'B-Flatulence/wind': 8, 'E-Flatulence/wind': 8, 'B-Severe_depression': 8, 'E-Severe_depression': 8, 'S-Advil': 8, 'B-Liver_enzymes_abnormal': 8, 'E-Liver_enzymes_abnormal': 8, 'B-Heel_pain': 8, 'E-Heel_pain': 8, \"I-Bell's_palsy\": 8, 'I-Tightness_in_throat': 8, 'I-Asthenia': 8, 'B-Poor_concentration': 8, 'E-Poor_concentration': 8, 'S-simvastatin': 8, 'I-Constant_pain': 8, 'B-omega-3-acid_ethyl_esters-90': 8, 'E-omega-3-acid_ethyl_esters-90': 8, 'I-Facial_swelling': 8, 'B-Joint_stiffness': 8, 'E-Joint_stiffness': 8, 'B-Increased_venous_pressure': 8, 'I-Increased_venous_pressure': 8, 'E-Increased_venous_pressure': 8, 'S-Aleve': 8, 'B-Excruciating_pain': 8, 'E-Excruciating_pain': 8, 'I-Impaired_exercise_tolerance': 8, 'E-Impaired_exercise_tolerance': 8, 'S-Neuropathy': 8, 'I-Acenaesthesia': 8, 'I-Ringing_in_ear': 8, 'S-Stress': 8, 'S-diclofenac': 8, 'I-Unsteady_when_standing': 8, 'I-Pain_in_left_leg': 8, 'B-Difficulty_breathing': 8, 'E-Difficulty_breathing': 8, 'I-Hand_joint_stiff': 8, 'B-Pain_in_toe': 8, 'E-Pain_in_toe': 8, 'I-Excruciating_pain': 8, 'B-Menstrual_cramp': 8, 'E-Menstrual_cramp': 8, 'I-Heavy_feeling': 8, 'B-Flushing': 8, 'E-Flushing': 8, 'I-Paraesthesia_of_foot': 8, 'I-Ovarian_pain': 8, 'I-Oedema_of_extremity': 8, 'I-Weight_gain': 8, 'I-Body_fluid_retention': 8, 'I-Spasm': 7, 'I-Peeling_of_skin': 7, 'I-Oedema_of_lower_extremity': 7, 'B-Burning_feet': 7, 'E-Burning_feet': 7, 'I-Cramp_in_limb': 7, 'I-Feeling_empty': 7, 'B-Kidney_disease': 7, 'E-Kidney_disease': 7, 'B-Myalgia/myositis_-_shoulder': 7, 'E-Myalgia/myositis_-_shoulder': 7, 'B-Generally_unwell': 7, 'E-Generally_unwell': 7, 'B-Rheumatoid_arthritis': 7, 'E-Rheumatoid_arthritis': 7, 'S-Lupus_erythematosus': 7, 'S-Migraine': 7, 'B-Pain_provoked_by_movement': 7, 'S-Influenza': 7, 'B-Vertigo': 7, 'E-Vertigo': 7, 'B-ubidecarenone': 7, 'E-ubidecarenone': 7, 'B-Lack_of_stamina': 7, 'E-Lack_of_stamina': 7, 'B-fish_oil_natural': 7, 'E-fish_oil_natural': 7, 'I-Unable_to_stand': 7, 'B-Paraesthesia_of_hand': 7, 'E-Paraesthesia_of_hand': 7, 'B-Depression': 7, 'E-Depression': 7, 'B-Palpitations': 7, 'E-Palpitations': 7, 'S-Tremor': 7, 'I-Heel_pain': 7, 'I-White_blood_cell_count_abnormal': 7, 'S-Moody': 7, 'B-Impaired_exercise_tolerance': 7, 'S-Delusional_disorder': 7, 'I-Dizziness': 7, 'S-Lethargy': 7, 'B-Insomnia': 7, 'E-Insomnia': 7, 'S-Menopause_present': 7, 'B-Random_blood_sugar_raised': 7, 'E-Random_blood_sugar_raised': 7, 'I-Pain_in_elbow': 7, 'E-Stiff_legs': 7, 'I-Feeling_faint': 7, 'S-Illness': 7, 'B-Unable_to_balance': 7, 'E-Unable_to_balance': 7, 'I-Malaise': 7, 'I-Abdominal_pain': 7, 'I-Lower_abdominal_pain': 7, 'I-Right_hemiparesis': 7, 'B-Swollen_ankle': 7, 'E-Swollen_ankle': 7, 'S-Multiple_sclerosis': 7, 'I-Large_breast': 7, 'I-Clicking_knee': 7, 'I-Symptom_of_ankle': 7, 'I-Initial_insomnia': 7, 'I-Increased_lactic_acid_level': 7, 'I-omega-3-acid_ethyl_esters-90': 7, 'I-Loss_of_interest': 7, 'I-Parkinsonian_tremor': 7, 'I-Abdominal_hyperaesthesia': 7, 'B-Tachycardia': 6, 'E-Tachycardia': 6, 'I-Erythema_of_skin': 6, 'B-Pulse_fast': 6, 'E-Pulse_fast': 6, 'S-Cough': 6, 'S-Sweating': 6, 'B-Dizziness': 6, 'E-Dizziness': 6, 'B-Tylenol': 6, 'E-Tylenol': 6, 'I-Dry_skin': 6, 'B-Muscle_rigidity': 6, 'E-Muscle_rigidity': 6, 'B-Menorrhagia': 6, 'E-Menorrhagia': 6, 'I-Lack_of_libido': 6, 'S-Spasm': 6, 'I-Lack_of_stamina': 6, 'E-Muscle_tension': 6, 'B-Unable_to_climb_stairs': 6, 'E-Unable_to_climb_stairs': 6, 'B-Weight_loss': 6, 'E-Weight_loss': 6, 'I-Swollen_legs': 6, 'B-Uterine_spasm': 6, 'E-Uterine_spasm': 6, 'I-Renal_impairment': 6, 'I-Gastrointestinal_tract_problem': 6, 'E-Myalgia/myositis_-_forearm': 6, 'B-Nausea': 6, 'E-Nausea': 6, 'S-Zoloft': 6, 'I-Shoulder_joint_pain': 6, 'E-Myalgia/myositis_-pelvis/thigh': 6, 'B-Spasm_of_back_muscles': 6, 'E-Spasm_of_back_muscles': 6, 'B-Nerve_injury': 6, 'E-Nerve_injury': 6, 'B-Hypertension': 6, 'E-Hypertension': 6, 'I-Unable_to_get_out_of_a_chair': 6, 'B-Slurred_speech': 6, 'E-Slurred_speech': 6, 'I-Knee_stiff': 6, 'B-Malaise': 6, 'E-Malaise': 6, 'B-Panic_attack': 6, 'E-Panic_attack': 6, 'B-Stomach_problem': 6, 'E-Stomach_problem': 6, 'B-Paraesthesia_of_lower_extremity': 6, 'E-Paraesthesia_of_lower_extremity': 6, 'S-ezetimibe_+_simvastatin': 6, 'E-Burning_pain': 6, 'S-Confusion': 6, 'S-Fever': 6, 'I-Pain_in_toe': 6, 'I-Weight_increased': 6, 'B-Numbness_of_hand': 6, 'E-Numbness_of_hand': 6, 'E-Flatulence,_eructation_and_gas_pain': 6, 'B-Numbness_of_lower_limb': 6, 'E-Numbness_of_lower_limb': 6, 'I-Unable_to_find_words': 6, 'I-Unable_to_lift': 6, 'I-Cramp_in_lower_leg': 6, 'I-Tendonitis_AND/OR_tenosynovitis_of_the_elbow_region': 6, 'I-Increased_liver_function': 6, 'I-Unable_to_lie_down': 6, 'I-Malignant_neoplasm_of_brain': 6, 'I-Numbness_of_face': 6, 'I-Tenalgia': 6, 'I-Reduced_mobility': 5, 'B-Spasm': 5, 'E-Spasm': 5, 'B-Dry_skin': 5, 'I-Swelling_of_finger': 5, 'E-Dry_skin': 5, 'B-Numbness_of_foot': 5, 'I-Numbness_of_foot': 5, 'E-Numbness_of_foot': 5, 'I-Glossitis': 5, 'I-Unable_to_move_leg': 5, 'B-cortisone': 5, 'E-cortisone': 5, 'B-alpha_tocopherol': 5, 'E-alpha_tocopherol': 5, 'B-Difficulty_swallowing': 5, 'E-Difficulty_swallowing': 5, 'I-Abdominal_colic': 5, 'S-Malignant_neoplastic_disease': 5, 'B-Tightness_in_throat': 5, 'E-Tightness_in_throat': 5, 'B-Renal_pain': 5, 'E-Renal_pain': 5, 'I-Non-smoker': 5, 'S-Lopid': 5, 'B-Spasmodic_movement': 5, 'I-Spasmodic_movement': 5, 'E-Spasmodic_movement': 5, \"B-Bell's_palsy\": 5, \"E-Bell's_palsy\": 5, 'I-Unsteady_when_walking': 5, 'B-Muscle_tension': 5, 'B-Dry_eyes': 5, 'E-Dry_eyes': 5, 'I-Serum_creatinine_raised': 5, 'B-Loss_of_appetite': 5, 'I-Loss_of_appetite': 5, 'E-Loss_of_appetite': 5, 'I-Chest_pain': 5, 'S-Forgetful': 5, 'B-Pain_provoked_by_walking': 5, 'E-Pain_provoked_by_walking': 5, 'B-Facial_swelling': 5, 'E-Facial_swelling': 5, 'B-Rib_pain': 5, 'E-Rib_pain': 5, 'I-Urinary_retention': 5, 'S-prednisone': 5, 'I-Difficulty_lifting': 5, 'S-Heartburn': 5, 'B-Foot_swelling': 5, 'E-Foot_swelling': 5, 'S-Disorientated': 5, 'S-Asthma': 5, 'B-Tight_chest': 5, 'E-Tight_chest': 5, 'B-Thoracic_back_pain': 5, 'E-Thoracic_back_pain': 5, 'B-Limitation_of_joint_movement': 5, 'E-Limitation_of_joint_movement': 5, 'I-Liver_disease': 5, 'B-Myalgia/myositis_-_forearm': 5, 'B-Heavy_legs': 5, 'E-Heavy_legs': 5, 'B-Myalgia/myositis_-pelvis/thigh': 5, 'B-Itching': 5, 'E-Itching': 5, 'I-Hypertension': 5, 'I-Excessive_and_frequent_menstruation': 5, 'B-Nasal_sinus_problem': 5, 'E-Nasal_sinus_problem': 5, 'B-Stiff_legs': 5, 'B-Confusion': 5, 'E-Confusion': 5, 'B-Loss_of_taste': 5, 'E-Loss_of_taste': 5, 'B-Pain_in_finger': 5, 'E-Pain_in_finger': 5, 'B-Knee_stiff': 5, 'E-Knee_stiff': 5, 'B-Stiffness': 5, 'E-Stiffness': 5, 'I-Reduction_of_bulk_of_muscle': 5, 'I-Respiratory_depression': 5, 'S-Myalgia': 5, 'B-Pain_in_throat': 5, 'E-Pain_in_throat': 5, 'I-Shoulder_stiff': 5, 'B-Pain_in_wrist': 5, 'E-Pain_in_wrist': 5, 'B-Weight_increased': 5, 'E-Weight_increased': 5, 'E-Arthralgia_of_the_lower_leg': 5, 'B-Stabbing_pain': 5, 'E-Stabbing_pain': 5, 'B-Type_2_diabetes_mellitus': 5, 'E-Type_2_diabetes_mellitus': 5, 'I-Impaired_mobility': 5, 'I-Joint_crepitus': 5, 'I-Dependance_on_walking_stick': 5, 'I-Flatulence,_eructation_and_gas_pain': 5, 'I-Poor_concentration': 5, 'B-Cramp_in_lower_leg': 5, 'E-Cramp_in_lower_leg': 5, 'I-Difficulty_moving_hand': 5, 'I-Acne': 5, 'B-Bizarre_dreams': 5, 'E-Bizarre_dreams': 5, 'I-Night_pain': 5, 'I-Peripheral_neuropathy': 5, 'I-Discomfort': 5, 'I-ubidecarenone': 5, 'S-Norvasc': 5, 'I-Hand_joint_pain': 5, 'I-Difficulty_getting_out_of_a_chair': 5, 'I-Blind_or_low_vision_-_one_eye_only': 5, 'I-Always_sleepy': 5, 'I-Amnesia': 5, 'I-Tightness_sensation': 5, 'I-Pain_provoked_by_rest': 5, 'I-Difficulty_standing': 5, 'B-Reduced_mobility': 4, 'E-Reduced_mobility': 4, 'B-Difficulty_walking_up_stairs': 4, 'E-Difficulty_walking_up_stairs': 4, 'B-Abdominal_bloating': 4, 'E-Abdominal_bloating': 4, 'S-Eczema': 4, 'B-Swelling_of_finger': 4, 'E-Swelling_of_finger': 4, 'B-Hand_cramps': 4, 'E-Hand_cramps': 4, 'B-Carpal_tunnel_syndrome': 4, 'E-Carpal_tunnel_syndrome': 4, 'S-aspirin': 4, 'S-Rhabdomyolysis': 4, 'B-Infective_cystitis': 4, 'E-Infective_cystitis': 4, 'S-Syncope': 4, 'B-Lack_of_libido': 4, 'E-Lack_of_libido': 4, 'S-Diovan': 4, 'B-Suicidal_thoughts': 4, 'E-Suicidal_thoughts': 4, 'B-Migraine': 4, 'E-Migraine': 4, 'B-Muscle_strain': 4, 'E-Muscle_strain': 4, 'B-Steatosis_of_liver': 4, 'E-Steatosis_of_liver': 4, 'B-Itching_of_skin': 4, 'E-Itching_of_skin': 4, 'I-Vertigo': 4, 'B-Sensation_of_heaviness_in_limbs': 4, 'E-Sensation_of_heaviness_in_limbs': 4, 'B-Abnormal_dreams': 4, 'E-Abnormal_dreams': 4, 'B-Swallowing_problem': 4, 'E-Swallowing_problem': 4, 'I-Hand_cramps': 4, 'I-Serum_triglycerides_raised': 4, 'B-Arthrotec': 4, 'E-Arthrotec': 4, 'B-Photosensitisation_due_to_sun': 4, 'E-Photosensitisation_due_to_sun': 4, 'B-Excessive_sweating': 4, 'E-Excessive_sweating': 4, 'I-Suicidal_thoughts': 4, 'B-Cramp': 4, 'E-Cramp': 4, 'B-Swollen_legs': 4, 'E-Swollen_legs': 4, \"I-Parkinson's_disease\": 4, 'I-Pounding_heart': 4, 'B-Swollen_knee': 4, 'E-Swollen_knee': 4, 'B-Limping': 4, 'E-Limping': 4, 'B-Numbness_of_upper_limb': 4, 'E-Numbness_of_upper_limb': 4, 'I-Lumbar_facet_joint_pain': 4, 'I-C-reactive_protein_abnormal': 4, 'B-Elbow_joint_pain': 4, 'E-Elbow_joint_pain': 4, 'B-Ringing_in_ear': 4, 'E-Ringing_in_ear': 4, 'B-Increased_creatine_kinase_level': 4, 'E-Increased_creatine_kinase_level': 4, 'S-Bronchitis': 4, 'B-Urine_looks_dark': 4, 'E-Urine_looks_dark': 4, 'I-Pain_in_finger': 4, 'I-Numbness': 4, 'I-Stiffness': 4, 'B-Hunger_pain': 4, 'E-Hunger_pain': 4, 'B-Generalised_acute_body_pains': 4, 'E-Generalised_acute_body_pains': 4, 'B-Tinnitus': 4, 'E-Tinnitus': 4, 'B-Haematochezia': 4, 'E-Haematochezia': 4, 'B-Dysphonia': 4, 'E-Dysphonia': 4, 'I-Limping': 4, 'B-Intervertebral_disc_prolapse': 4, 'E-Intervertebral_disc_prolapse': 4, 'B-Arthralgia_of_the_lower_leg': 4, 'B-Hand_joint_stiff': 4, 'E-Hand_joint_stiff': 4, 'I-Abdominal_wind_pain': 4, 'B-Gastric_ulcer': 4, 'E-Gastric_ulcer': 4, 'I-Difficulty_breathing': 4, 'S-Malaise': 4, 'I-Fine_motor_impairment': 4, 'I-Arthritis_of_hip': 4, 'I-Joint_stiffness': 4, 'B-Flatulence,_eructation_and_gas_pain': 4, 'S-Reflux': 4, 'I-Urine:_dark/concentrated': 4, 'I-Blistering_rash': 4, 'I-Mucosal_numbness': 4, 'B-Unable_to_move_arm': 4, 'E-Unable_to_move_arm': 4, 'I-Tingling_of_skin': 4, 'S-Palpitations': 4, 'I-Decreased_cholesterol_esters': 4, 'I-Heavy_episode_of_vaginal_bleeding': 4, 'B-Diarrhoea': 4, 'E-Diarrhoea': 4, 'I-Numbness_and_tingling_sensation_of_skin': 4, 'S-Limping': 4, 'I-Rupture_of_tendon_of_lower_leg_and_ankle': 4, 'I-Hyperaesthesia': 4, 'I-Impaired_bed_mobility': 4, 'B-Malignant_neoplasm_of_brain': 4, 'E-Malignant_neoplasm_of_brain': 4, 'I-Coronary_arteriosclerosis': 4, 'B-Tremor': 4, 'I-Tremor': 4, 'E-Tremor': 4, 'S-Backache': 4, 'B-Acute_pain': 4, 'E-Acute_pain': 4, 'I-Nerve_injury': 4, 'I-Unable_to_move_all_four_limbs': 4, 'I-Hyperaemia_of_surface_of_eye': 4, 'I-Difficulty_dressing': 4, 'I-Feels_everything_is_futile': 4, 'B-Loose_stool': 3, 'E-Loose_stool': 3, 'I-Mood_swings': 3, 'B-Peeling_of_skin': 3, 'B-Incoordination': 3, 'E-Incoordination': 3, 'I-Propensity_to_adverse_reactions_to_drug': 3, 'B-Numbness_of_limbs': 3, 'E-Numbness_of_limbs': 3, 'B-Stiff_limbs': 3, 'I-Stiff_limbs': 3, 'E-Stiff_limbs': 3, 'I-Unable_to_walk_down_stairs': 3, 'I-Chronic_back_pain': 3, 'B-Arthritis': 3, 'B-Myositis': 3, 'I-Arthritis': 3, 'I-Myositis': 3, 'E-Myositis': 3, 'E-Arthritis': 3, 'B-Disorder_of_foot': 3, 'E-Disorder_of_foot': 3, 'B-Cramp_in_limb': 3, 'E-Cramp_in_limb': 3, 'S-Spasmodic_movement': 3, 'B-Moody': 3, 'E-Moody': 3, 'B-Increased_appetite': 3, 'E-Increased_appetite': 3, 'B-Feeling_empty': 3, 'E-Feeling_empty': 3, 'B-Irregular_menstruation': 3, 'E-Irregular_menstruation': 3, 'S-Hypertension': 3, 'S-Nightmares': 3, 'S-metoprolol': 3, 'B-Body_fluid_retention': 3, 'E-Body_fluid_retention': 3, 'S-Blister': 3, 'I-Forgetful': 3, 'I-Urinary_tract_infection': 3, 'B-Bone_pain': 3, 'E-Bone_pain': 3, 'B-Digestive_symptom': 3, 'E-Digestive_symptom': 3, 'B-Scalp_itchy': 3, 'E-Scalp_itchy': 3, 'B-Myalgia/myositis_-_upper_arm': 3, 'E-Myalgia/myositis_-_upper_arm': 3, 'B-Exhaustion': 3, 'E-Exhaustion': 3, 'S-Feeling_unhappy': 3, 'I-Dry_eyes': 3, 'I-Right_upper_quadrant_pain': 3, 'I-Liver_function_tests_abnormal': 3, 'B-Serum_triglycerides_raised': 3, 'E-Serum_triglycerides_raised': 3, 'I-Deteriorating_renal_function': 3, 'S-Lipex': 3, 'S-Itching_of_skin': 3, 'B-Hoarse': 3, 'E-Hoarse': 3, 'S-misoprostol': 3, 'B-Cold_sweat': 3, 'E-Cold_sweat': 3, 'B-Renal_impairment': 3, 'E-Renal_impairment': 3, \"B-Parkinson's_disease\": 3, \"E-Parkinson's_disease\": 3, 'S-Neurontin': 3, 'I-Neuroma_of_foot': 3, 'B-Swelling_of_knee_joint': 3, 'E-Swelling_of_knee_joint': 3, 'B-Difficulty_getting_off_a_bed': 3, 'E-Difficulty_getting_off_a_bed': 3, 'S-Chill': 3, 'B-Taste_sense_altered': 3, 'E-Taste_sense_altered': 3, 'B-Personality_change': 3, 'E-Personality_change': 3, 'B-Sleep_deprivation': 3, 'I-Sleep_deprivation': 3, 'E-Sleep_deprivation': 3, 'I-Exhaustion': 3, 'S-Sciatica': 3, 'B-Shoulder_joint_pain': 3, 'E-Shoulder_joint_pain': 3, 'I-Fear': 3, 'I-Disorder_of_nasal_sinus': 3, 'I-Pain_in_limb': 3, 'B-Sensation_of_hot_and_cold': 3, 'E-Sensation_of_hot_and_cold': 3, 'S-Endometriosis': 3, 'B-Renal_failure': 3, 'E-Renal_failure': 3, 'I-Cold_sweat': 3, 'B-Bleeding_between_periods': 3, 'E-Bleeding_between_periods': 3, 'B-Sharp_pain': 3, 'E-Sharp_pain': 3, 'B-Cough': 3, 'E-Cough': 3, 'B-Numbness': 3, 'E-Numbness': 3, 'B-Disorder_of_eye': 3, 'E-Disorder_of_eye': 3, 'I-Hip_stiff': 3, 'S-Discomfort': 3, 'I-Slow_on_legs': 3, 'I-Unable_to_stand_up': 3, 'I-Weight_loss': 3, 'B-Rectal_haemorrhage': 3, 'E-Rectal_haemorrhage': 3, 'S-Colitis': 3, 'S-atorvastatin': 3, 'I-Dysphonia': 3, 'I-Unable_to_eat': 3, 'B-Burning_pain': 3, 'S-Hot_sweats': 3, 'B-Sinusitis': 3, 'E-Sinusitis': 3, 'S-Respiratory_tract_congestion': 3, 'B-Bipolar_disorder': 3, 'E-Bipolar_disorder': 3, 'S-Osteoarthritis': 3, 'I-Oedema_of_finger': 3, 'S-Menstrual_spotting': 3, 'E-Low_blood_pressure_reading': 3, 'B-Raised_low_density_lipoprotein_cholesterol': 3, 'E-Raised_low_density_lipoprotein_cholesterol': 3, 'S-potassium': 3, 'B-Always_hungry': 3, 'E-Always_hungry': 3, 'I-Joint_laxity': 3, 'I-Lacks_confidence': 3, 'B-Lateral_epicondylitis': 3, 'E-Lateral_epicondylitis': 3, 'B-Hypertonia': 3, 'E-Hypertonia': 3, 'I-Arthritis_of_knee': 3, 'I-Type_II_diabetes_mellitus_uncontrolled': 3, 'S-morphine': 3, 'I-Type_2_diabetes_mellitus': 3, 'B-Chronic_fatigue_syndrome': 3, 'E-Chronic_fatigue_syndrome': 3, 'B-Feeling_irritable': 3, 'E-Feeling_irritable': 3, 'I-Aptyalism': 3, 'I-Influenza-like_symptoms': 3, 'B-Lower_abdominal_pain': 3, 'E-Lower_abdominal_pain': 3, 'I-Increased_thirst': 3, 'B-Heartburn': 3, 'E-Heartburn': 3, 'B-Numbness_of_face': 3, 'E-Numbness_of_face': 3, 'B-Urine:_dark/concentrated': 3, 'E-Urine:_dark/concentrated': 3, 'I-Swollen_nasal_mucosa': 3, 'B-Loss_of_interest': 3, 'E-Loss_of_interest': 3, 'B-Weakness_of_hand': 3, 'E-Weakness_of_hand': 3, 'E-Blistering_rash': 3, 'I-fish_oil_natural': 3, 'I-Arthralgia_of_multiple_joints': 3, 'I-Decreased_muscle_tone': 3, 'I-Wrist_joint_pain': 3, 'I-Middle_insomnia': 3, 'I-Swelling_of_knee_joint': 3, 'I-Hypertonia': 3, 'B-Tingling_of_skin': 3, 'E-Tingling_of_skin': 3, 'B-Achilles_tendonitis': 3, 'E-Achilles_tendonitis': 3, 'B-Arthritis_of_hand': 3, 'E-Arthritis_of_hand': 3, 'I-Swollen_ankle': 3, 'I-Loss_of_confidence': 3, 'I-Contusion_of_hand': 3, 'I-Shoulder_tendonitis': 3, 'S-Disorder_of_muscle': 3, 'I-Heartburn': 3, 'S-codeine': 3, 'B-Increased_liver_function': 3, 'E-Increased_liver_function': 3, 'I-Pancreatitis': 3, 'B-Initial_insomnia': 3, 'E-Initial_insomnia': 3, 'I-Pain_in_right_arm': 3, 'B-Numbness_and_tingling_sensation_of_skin': 3, 'E-Numbness_and_tingling_sensation_of_skin': 3, 'I-Renal_pain': 3, 'B-Unable_to_walk_up_step': 3, 'E-Unable_to_walk_up_step': 3, 'B-Irregular_heart_beat': 3, 'E-Irregular_heart_beat': 3, 'B-Facial_palsy': 3, 'E-Facial_palsy': 3, 'I-Muscle_injury': 3, 'I-Warm_hands': 3, 'I-Compression_neuropathy_of_trunk': 3, 'I-Muscle_fasciculation': 3, 'I-Musculoskeletal_pain': 3, 'I-Yellow_skin': 3, 'I-Intolerant_of_cold': 3, 'I-Unable_to_speak': 3, 'B-Coronary_arteriosclerosis': 3, 'E-Coronary_arteriosclerosis': 3, 'I-Knee_joint_painful_on_movement': 3, 'S-Herpes_zoster': 3, 'I-Numbness_of_toe': 3, 'S-paracetamol': 3, 'I-Ankle_joint_pain': 3, 'I-Unable_to_move_hand': 3, 'I-alpha_tocopherol': 3, 'I-Iliotibial_band_friction_syndrome': 3, 'I-Malaise_and_fatigue': 3, 'S-Bursitis': 3, 'I-Achilles_tendonitis': 3, 'I-Right_ventricular_dilatation': 3, 'I-Strain_of_calf_muscle': 3, 'I-Muscle_strain': 3, 'I-Intervertebral_disc_prolapse': 3, 'I-Abdominal_distension': 3, 'I-Pain_in_cheek': 3, 'I-Skin_nodule': 3, 'B-Erythema_of_skin': 2, 'E-Erythema_of_skin': 2, 'E-Peeling_of_skin': 2, 'B-Oedema_of_lower_extremity': 2, 'E-Oedema_of_lower_extremity': 2, 'B-Unable_to_move_leg': 2, 'E-Unable_to_move_leg': 2, 'I-Tylenol': 2, 'B-Chronic_back_pain': 2, 'E-Chronic_back_pain': 2, 'I-Carpal_tunnel_syndrome': 2, 'S-glucosamine': 2, 'B-Intercostal_myalgia': 2, 'E-Intercostal_myalgia': 2, 'B-Influenza': 2, 'E-Influenza': 2, 'I-Flatulence/wind': 2, 'B-Nonspecific_abdominal_symptom': 2, 'E-Nonspecific_abdominal_symptom': 2, 'B-Chest_discomfort': 2, 'E-Chest_discomfort': 2, 'I-Chronic_intractable_pain': 2, 'S-Muscle_atrophy': 2, 'S-Clumsiness': 2, 'B-Hepatic_trauma': 2, 'I-Hepatic_trauma': 2, 'E-Hepatic_trauma': 2, 'S-Zestril': 2, 'S-Haematuria': 2, 'I-Collapse': 2, 'B-Forgetful': 2, 'E-Forgetful': 2, 'B-Urinary_tract_infection': 2, 'E-Urinary_tract_infection': 2, 'I-Urgent_desire_to_urinate': 2, 'B-Non-smoker': 2, 'E-Non-smoker': 2, 'B-Failure_to_lose_weight': 2, 'E-Failure_to_lose_weight': 2, 'B-Type_1_diabetes_mellitus': 2, 'I-Type_1_diabetes_mellitus': 2, 'E-Type_1_diabetes_mellitus': 2, 'I-Myocardial_infarction': 2, 'B-Derealisation': 2, 'E-Derealisation': 2, 'B-Allergic_reaction': 2, 'E-Allergic_reaction': 2, 'I-Scalp_itchy': 2, 'I-Lip_swelling': 2, 'B-Discolouration_of_skin': 2, 'E-Discolouration_of_skin': 2, 'I-Unable_to_make_a_fist': 2, 'I-Impairment_level_of_vision': 2, 'B-Unsteady_when_walking': 2, 'E-Unsteady_when_walking': 2, 'S-Jaundice': 2, 'B-Gallbladder_disorder': 2, 'I-Gallbladder_disorder': 2, 'E-Gallbladder_disorder': 2, 'S-Mentally_dull': 2, 'B-Rupture_of_tendon': 2, 'E-Rupture_of_tendon': 2, 'B-Rupture_of_Achilles_tendon': 2, 'E-Rupture_of_Achilles_tendon': 2, 'I-Rupture_of_tendon': 2, 'B-Unable_to_stand': 2, 'E-Unable_to_stand': 2, 'B-Feels_hot': 2, 'E-Feels_hot': 2, 'S-Loss_of_appetite': 2, 'I-Cardiomyopathy': 2, 'B-Pain_in_thumb': 2, 'E-Pain_in_thumb': 2, 'I-Pain_in_thumb': 2, 'B-Burning_sensation_of_vagina': 2, 'I-Burning_sensation_of_vagina': 2, 'E-Burning_sensation_of_vagina': 2, 'B-Mastalgia': 2, 'E-Mastalgia': 2, 'S-Erythema': 2, 'I-Cut_of_hand': 2, 'I-Hoarse': 2, 'B-Lipitor_40_mg_tablet:_film-coated': 2, 'E-Lipitor_40_mg_tablet:_film-coated': 2, 'B-Urinary_retention': 2, 'E-Urinary_retention': 2, 'S-Feeling_intoxicated': 2, 'B-Difficulty_lifting': 2, 'E-Difficulty_lifting': 2, 'I-Bad_taste_in_mouth': 2, 'B-Hammer_toe': 2, 'E-Hammer_toe': 2, 'I-Lopid': 2, 'B-White_blood_cell_count_abnormal': 2, 'E-White_blood_cell_count_abnormal': 2, 'I-Tendonitis_of_knee': 2, 'I-Drinks_wine': 2, 'B-Injury_of_muscle': 2, 'E-Injury_of_muscle': 2, 'I-Neuropathy_of_upper_limb': 2, 'E-Neuropathy_of_upper_limb': 2, 'B-Swelling_of_hand': 2, 'E-Swelling_of_hand': 2, 'S-Allergic_condition': 2, 'S-Tachycardia': 2, 'B-General_unsteadiness': 2, 'E-General_unsteadiness': 2, 'B-Ankle_oedema': 2, 'E-Ankle_oedema': 2, 'S-Lipidil': 2, 'I-Menstrual_loss_increasing': 2, 'B-Urinary_incontinence': 2, 'E-Urinary_incontinence': 2, 'B-Major_depressive_disorder': 2, 'E-Major_depressive_disorder': 2, 'B-Fear': 2, 'E-Fear': 2, 'I-Itching': 2, 'S-Haemorrhoids': 2, 'B-C-reactive_protein_abnormal': 2, 'E-C-reactive_protein_abnormal': 2, 'I-Pain_in_spine': 2, 'I-Bursitis_of_hip': 2, 'S-Aphasia': 2, 'S-Loss_of_hair': 2, 'I-Spots_on_skin': 2, 'B-Visual_impairment': 2, 'E-Visual_impairment': 2, 'I-Sacroiliac_joint_stiff': 2, 'I-Increased_creatine_kinase_level': 2, 'B-Tendonitis': 2, 'I-Tendonitis': 2, 'E-Tendonitis': 2, 'I-Sense_of_smell_impaired': 2, 'S-Anaemia': 2, 'B-Frequent_defaecation': 2, 'I-Frequent_defaecation': 2, 'E-Frequent_defaecation': 2, 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'E-Pruritus_ani': 1, 'B-Paraesthesia_of_upper_limb': 1, 'I-Paraesthesia_of_upper_limb': 1, 'E-Paraesthesia_of_upper_limb': 1, 'B-Iliotibial_band_friction_syndrome': 1, 'E-Iliotibial_band_friction_syndrome': 1, 'B-Malaise_and_fatigue': 1, 'E-Malaise_and_fatigue': 1, 'S-Panadeine': 1, 'B-codeine_+_ibuprofen': 1, 'E-codeine_+_ibuprofen': 1, 'B-Voltaren_Rapid_25_mg_tablet:_sugar-coated': 1, 'I-Voltaren_Rapid_25_mg_tablet:_sugar-coated': 1, 'E-Voltaren_Rapid_25_mg_tablet:_sugar-coated': 1, 'S-Stumbling_gait': 1, 'S-Lescol': 1, 'B-Irritable_bowel_syndrome': 1, 'E-Irritable_bowel_syndrome': 1, 'B-Indigestion': 1, 'E-Indigestion': 1, 'S-Psychotic_disorder': 1, 'I-Stomach_problem': 1, 'S-Seroquel': 1, 'B-Sweating_problem': 1, 'E-Sweating_problem': 1, 'B-Immobile': 1, 'E-Immobile': 1, 'B-Arthritis/arthrosis': 1, 'E-Arthritis/arthrosis': 1, 'B-Neuralgia': 1, 'E-Neuralgia': 1, 'S-Prinivil': 1, 'B-Shin_splint': 1, 'E-Shin_splint': 1, 'S-Paraesthesia': 1, 'B-Right_ventricular_dilatation': 1, 'E-Right_ventricular_dilatation': 1, 'B-Raised_serum_calcium_level': 1, 'E-Raised_serum_calcium_level': 1, 'B-Disorder_of_cardiac_function': 1, 'E-Disorder_of_cardiac_function': 1, 'S-magnesium': 1, 'B-Feeling_content': 1, 'I-Feeling_content': 1, 'E-Feeling_content': 1, 'S-Pain_in_upper_limb': 1, 'B-Bleeding_from_nose': 1, 'E-Bleeding_from_nose': 1, 'B-Nocturnal_enuresis': 1, 'E-Nocturnal_enuresis': 1, 'I-Musculoskeletal_chest_pain': 1, 'B-Numbness_of_finger': 1, 'I-Numbness_of_finger': 1, 'E-Numbness_of_finger': 1, 'B-Difficulty_gripping': 1, 'E-Difficulty_gripping': 1, 'B-Eye_strain': 1, 'I-Eye_strain': 1, 'E-Eye_strain': 1, 'B-Unable_to_sit': 1, 'I-Unable_to_sit': 1, 'E-Unable_to_sit': 1, 'B-Abdominal_hyperaesthesia': 1, 'E-Abdominal_hyperaesthesia': 1, 'B-Difficulty_moving_arm': 1, 'E-Difficulty_moving_arm': 1, 'B-Feeling_agitated_232_249': 1, 'E-Feeling_agitated_232_249': 1, 'B-Unable_to_control_anger': 1, 'I-Unable_to_control_anger': 1, 'E-Unable_to_control_anger': 1, 'S-Disorder_of_tendon': 1, 'B-Tendon_contracture': 1, 'E-Tendon_contracture': 1, 'B-Derangement_of_meniscus': 1, 'E-Derangement_of_meniscus': 1, 'B-Aphasia': 1, 'E-Aphasia': 1, 'S-Pancreatitis': 1, 'B-Cardiovascular_disease': 1, 'E-Cardiovascular_disease': 1, 'B-Prolonged_periods': 1, 'E-Prolonged_periods': 1, 'B-Bad_circulation_-_vasomotor_change': 1, 'E-Bad_circulation_-_vasomotor_change': 1, 'B-Pain_provoked_by_rest': 1, 'E-Pain_provoked_by_rest': 1, 'B-Chronic_diarrhoea': 1, 'E-Chronic_diarrhoea': 1, 'B-Ulcerative_colitis': 1, 'E-Ulcerative_colitis': 1, 'B-Angina': 1, 'E-Angina': 1, 'B-Lupus_erythematosus': 1, 'E-Lupus_erythematosus': 1, 'S-Capoten': 1, 'S-glipizide': 1, 'S-tramadol': 1, 'B-Not_getting_enough_sleep': 1, 'E-Not_getting_enough_sleep': 1, 'B-Myalgia/myositis_-_multiple': 1, 'E-Myalgia/myositis_-_multiple': 1, 'B-White_discolouration_of_skin': 1, 'E-White_discolouration_of_skin': 1, 'B-Difficulty_dressing': 1, 'E-Difficulty_dressing': 1, 'B-Traumatic_blister_of_mouth': 1, 'E-Traumatic_blister_of_mouth': 1, 'B-Abdominal_distension': 1, 'E-Abdominal_distension': 1, 'B-Pain_in_cheek': 1, 'E-Pain_in_cheek': 1, 'S-pyridoxine': 1, 'B-Increased_albumin': 1, 'I-Increased_albumin': 1, 'E-Increased_albumin': 1, 'B-Gingivitis': 1, 'E-Gingivitis': 1, 'B-Post_traumatic_osteoarthritis': 1, 'I-Post_traumatic_osteoarthritis': 1, 'E-Post_traumatic_osteoarthritis': 1, 'B-Dropped_beats_-_heart': 1, 'I-Dropped_beats_-_heart': 1, 'E-Dropped_beats_-_heart': 1, 'B-Cardiac_arrhythmia': 1, 'E-Cardiac_arrhythmia': 1, 'I-Irregular_heart_beat': 1, 'B-Pain_of_uterus': 1, 'E-Pain_of_uterus': 1, 'B-Anxiety_attack': 1, 'I-Anxiety_attack': 1, 'E-Anxiety_attack': 1, 'B-Difficulty_riding_a_bicycle': 1, 'I-Difficulty_riding_a_bicycle': 1, 'E-Difficulty_riding_a_bicycle': 1, 'B-Difficulty_standing': 1, 'E-Difficulty_standing': 1, 'B-Foot_eczema': 1, 'B-Hand_eczema': 1, 'I-Foot_eczema': 1, 'I-Hand_eczema': 1, 'E-Hand_eczema': 1, 'E-Foot_eczema': 1, 'S-Hypochondriasis': 1, 'B-Vaginospasm': 1, 'I-Vaginospasm': 1, 'E-Vaginospasm': 1, 'B-Knee_joint_inflamed': 1, 'E-Knee_joint_inflamed': 1, 'B-Sinus_pain': 1, 'E-Sinus_pain': 1, 'B-Voltaren_Rapid': 1, 'E-Voltaren_Rapid': 1, 'B-Skin_nodule': 1, 'E-Skin_nodule': 1, 'B-Stress_overload': 1, 'E-Stress_overload': 1, 'S-Feeling_hopeless': 1, 'S-Feeling_lonely': 1, 'B-Poor_self-image': 1, 'E-Poor_self-image': 1, 'B-Morbid_thoughts': 1, 'E-Morbid_thoughts': 1, 'B-Feels_everything_is_futile': 1, 'E-Feels_everything_is_futile': 1, 'B-Transient_global_amnesia': 1, 'I-Transient_global_amnesia': 1, 'E-Transient_global_amnesia': 1})\n"
     ]
    }
   ],
   "source": [
    "# get all unique ner tags, excluding leading conll ordering term\n",
    "ner_tags = []\n",
    "for doc in train_docs:\n",
    "    for token in doc:\n",
    "        labels = get_all_labels(token)\n",
    "        if \"ner\" in labels:\n",
    "            for label in labels[\"ner\"]:\n",
    "                ner_tags.append(label.value)\n",
    "\n",
    "# get all unique meddra tags, excluding leading conll ordering term\n",
    "meddra_tags = []\n",
    "for doc in train_docs:\n",
    "    for token in doc:\n",
    "        labels = get_all_labels(token)\n",
    "        if \"meddra\" in labels:\n",
    "            for label in labels[\"meddra\"]:\n",
    "                meddra_tags.append(label.value)\n",
    "ner_tags = Counter(ner_tags)\n",
    "del ner_tags['O']\n",
    "meddra_tags = Counter(meddra_tags)\n",
    "del meddra_tags['O']\n",
    "\n",
    "ner_tags_ordered = [a[0] for a in ner_tags.most_common()]\n",
    "meddra_tags_ordered = [a[0] for a in meddra_tags.most_common()]\n",
    "\n",
    "print(\"Unique NER tags:\", ner_tags)\n",
    "print(\"Unique Meddra tags:\", meddra_tags)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ner_tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tab = pd.DataFrame([[a, b] for a,b in ner_tags.items()], columns = ['NER tag','count'])\n",
    "tab = tab.sort_values('count',ascending=False)\n",
    "\n",
    "\n",
    "sns.set(rc={'figure.figsize':(10,6)})\n",
    "chart = sns.barplot(tab, x='NER tag',y='count')\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=60, horizontalalignment='right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tab = pd.DataFrame([[a, b] for a,b in meddra_tags.items()], columns = ['mapped term','count'])\n",
    "tab = tab.sort_values('count',ascending=False)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(10,6)})\n",
    "chart = sns.barplot(tab[:20], x='mapped term',y='count')\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=60, horizontalalignment='right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "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>mapped term</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>S-Lipitor</td>\n",
       "      <td>825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>I-CONCEPT_LESS</td>\n",
       "      <td>514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>S-Pain</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I-Severe_pain</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>E-CONCEPT_LESS</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>S-soy_lecithin</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>E-Daily_headache</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>I-Daily_headache</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>B-Daily_headache</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2449</th>\n",
       "      <td>E-Transient_global_amnesia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2450 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     mapped term  count\n",
       "9                      S-Lipitor    825\n",
       "59                I-CONCEPT_LESS    514\n",
       "12                        S-Pain    275\n",
       "1                  I-Severe_pain    265\n",
       "60                E-CONCEPT_LESS    232\n",
       "...                          ...    ...\n",
       "836               S-soy_lecithin      1\n",
       "835             E-Daily_headache      1\n",
       "834             I-Daily_headache      1\n",
       "833             B-Daily_headache      1\n",
       "2449  E-Transient_global_amnesia      1\n",
       "\n",
       "[2450 rows x 2 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def repeat_arr(lst, n):\n",
    "    if len(lst) < n:\n",
    "        lst *= (n // len(lst)) + 1\n",
    "    return lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_o_tags(y_true, y_pred):\n",
    "    # y_true_no_o = []\n",
    "    # y_pred_no_o = []\n",
    "    # for true, pred in zip(y_true, y_pred):\n",
    "    #     if true != 'O':\n",
    "    #         y_true_no_o.append(true)\n",
    "    #         y_pred_no_o.append(pred)\n",
    "    # return y_true_no_o, y_pred_no_o\n",
    "    \n",
    "    return y_true, y_pred\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ner - random baseline"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ner - most frequent baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 948/948 [00:06<00:00, 146.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I-ADR\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 119/119 [00:00<00:00, 145.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I-ADR\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 119/119 [00:00<00:00, 154.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I-ADR\n"
     ]
    }
   ],
   "source": [
    "res_random = res_mostfreq = pd.DataFrame(columns=['split','index']+ner_tags_ordered)\n",
    "train=None\n",
    "for split, split_name in [(train_docs,'train'),(dev_docs,'dev'),(test_docs,'test')]:\n",
    "    y_true = []\n",
    "    for doc in tqdm(split, total=len(split)):\n",
    "        for token in doc:\n",
    "            labels = get_all_labels(token)\n",
    "            y_true += [a.value for a in labels['ner']]\n",
    "            \n",
    "    if split_name == 'train':\n",
    "        train = y_true\n",
    "            \n",
    "    np.random.seed(1234)\n",
    "    y_pred = random.sample(repeat_arr(sorted(ner_tags_ordered+['O']), len(y_true)), len(y_true))\n",
    "    y_true1, y_pred1 = remove_o_tags(y_true, y_pred)\n",
    "    res = pd.DataFrame.from_dict(classification_report(y_true1, y_pred1, output_dict=True, zero_division=0)).reset_index()\n",
    "    temp = res['index']\n",
    "    res.drop('index',axis=1,inplace=True)\n",
    "    res.insert(loc=0, column='split', value=split_name)\n",
    "    res.insert(loc=1, column='index', value=temp)\n",
    "    res_random = pd.concat([res_random, res],ignore_index=True)\n",
    "    \n",
    "    mostfreq = ner_tags_ordered[0]\n",
    "    print(mostfreq)\n",
    "    y_pred = [mostfreq]*len(y_true)\n",
    "    y_true1, y_pred1 = remove_o_tags(y_true, y_pred)\n",
    "    res = pd.DataFrame.from_dict(classification_report(y_true1, y_pred1, output_dict=True, zero_division=0)).reset_index()\n",
    "    temp = res['index']\n",
    "    res.drop('index',axis=1,inplace=True)\n",
    "    res.insert(loc=0, column='split', value=split_name)\n",
    "    res.insert(loc=1, column='index', value=temp)\n",
    "    res_mostfreq = pd.concat([res_mostfreq, res],ignore_index=True)\n",
    "\n",
    "if 'O' in res_random.columns:\n",
    "    res_random.drop('O',axis=1,inplace=True)\n",
    "if 'O' in res_mostfreq.columns:\n",
    "    res_mostfreq.drop('O',axis=1,inplace=True)\n",
    "\n",
    "res_random = res_random.iloc[:, :res_random.columns.get_loc('weighted avg')+1]\n",
    "res_mostfreq = res_mostfreq.iloc[:, :res_mostfreq.columns.get_loc('weighted avg')+1]\n",
    "\n",
    "res_random.to_csv('baseline_ner_model'+suf+'/random_classification_report.csv',index=False)\n",
    "res_mostfreq.to_csv('baseline_ner_model'+suf+'/mostfreq_classification_report.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "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>split</th>\n",
       "      <th>index</th>\n",
       "      <th>I-ADR</th>\n",
       "      <th>E-ADR</th>\n",
       "      <th>B-ADR</th>\n",
       "      <th>S-ADR</th>\n",
       "      <th>S-Drug</th>\n",
       "      <th>B-Finding</th>\n",
       "      <th>E-Finding</th>\n",
       "      <th>I-Finding</th>\n",
       "      <th>...</th>\n",
       "      <th>E-Drug</th>\n",
       "      <th>B-Symptom</th>\n",
       "      <th>B-Drug</th>\n",
       "      <th>S-Finding</th>\n",
       "      <th>I-Symptom</th>\n",
       "      <th>I-Drug</th>\n",
       "      <th>I-Disease</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>macro avg</th>\n",
       "      <th>weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train</td>\n",
       "      <td>precision</td>\n",
       "      <td>0.046754</td>\n",
       "      <td>0.036996</td>\n",
       "      <td>0.037456</td>\n",
       "      <td>0.017952</td>\n",
       "      <td>0.010419</td>\n",
       "      <td>0.002215</td>\n",
       "      <td>0.001994</td>\n",
       "      <td>0.001108</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000665</td>\n",
       "      <td>0.000886</td>\n",
       "      <td>0.001108</td>\n",
       "      <td>0.001108</td>\n",
       "      <td>0.000886</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>0.047511</td>\n",
       "      <td>0.047514</td>\n",
       "      <td>0.697116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train</td>\n",
       "      <td>recall</td>\n",
       "      <td>0.047426</td>\n",
       "      <td>0.047633</td>\n",
       "      <td>0.050463</td>\n",
       "      <td>0.059428</td>\n",
       "      <td>0.036547</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.045000</td>\n",
       "      <td>0.033557</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.036364</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.045872</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.047511</td>\n",
       "      <td>0.047094</td>\n",
       "      <td>0.047511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train</td>\n",
       "      <td>f1-score</td>\n",
       "      <td>0.047088</td>\n",
       "      <td>0.041646</td>\n",
       "      <td>0.042997</td>\n",
       "      <td>0.027574</td>\n",
       "      <td>0.016215</td>\n",
       "      <td>0.004243</td>\n",
       "      <td>0.003818</td>\n",
       "      <td>0.002145</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001297</td>\n",
       "      <td>0.001730</td>\n",
       "      <td>0.002163</td>\n",
       "      <td>0.002163</td>\n",
       "      <td>0.001732</td>\n",
       "      <td>0.000438</td>\n",
       "      <td>0.000877</td>\n",
       "      <td>0.047511</td>\n",
       "      <td>0.014366</td>\n",
       "      <td>0.080656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train</td>\n",
       "      <td>support</td>\n",
       "      <td>4449.000000</td>\n",
       "      <td>3506.000000</td>\n",
       "      <td>3349.000000</td>\n",
       "      <td>1363.000000</td>\n",
       "      <td>1286.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.047511</td>\n",
       "      <td>94779.000000</td>\n",
       "      <td>94779.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dev</td>\n",
       "      <td>precision</td>\n",
       "      <td>0.041051</td>\n",
       "      <td>0.042693</td>\n",
       "      <td>0.037829</td>\n",
       "      <td>0.014803</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>0.001642</td>\n",
       "      <td>0.001645</td>\n",
       "      <td>0.001645</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003295</td>\n",
       "      <td>0.001642</td>\n",
       "      <td>0.001650</td>\n",
       "      <td>0.003289</td>\n",
       "      <td>0.003289</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047906</td>\n",
       "      <td>0.047991</td>\n",
       "      <td>0.709215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dev</td>\n",
       "      <td>recall</td>\n",
       "      <td>0.048356</td>\n",
       "      <td>0.057906</td>\n",
       "      <td>0.052036</td>\n",
       "      <td>0.043062</td>\n",
       "      <td>0.042683</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047906</td>\n",
       "      <td>0.041616</td>\n",
       "      <td>0.047906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dev</td>\n",
       "      <td>f1-score</td>\n",
       "      <td>0.044405</td>\n",
       "      <td>0.049149</td>\n",
       "      <td>0.043810</td>\n",
       "      <td>0.022032</td>\n",
       "      <td>0.018111</td>\n",
       "      <td>0.003086</td>\n",
       "      <td>0.003115</td>\n",
       "      <td>0.003185</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006380</td>\n",
       "      <td>0.003221</td>\n",
       "      <td>0.003195</td>\n",
       "      <td>0.006319</td>\n",
       "      <td>0.006339</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047906</td>\n",
       "      <td>0.014557</td>\n",
       "      <td>0.081262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>dev</td>\n",
       "      <td>support</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>449.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.047906</td>\n",
       "      <td>12775.000000</td>\n",
       "      <td>12775.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>test</td>\n",
       "      <td>precision</td>\n",
       "      <td>0.037300</td>\n",
       "      <td>0.026596</td>\n",
       "      <td>0.039007</td>\n",
       "      <td>0.014184</td>\n",
       "      <td>0.015986</td>\n",
       "      <td>0.005319</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005319</td>\n",
       "      <td>0.008865</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001776</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047144</td>\n",
       "      <td>0.047117</td>\n",
       "      <td>0.696345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>test</td>\n",
       "      <td>recall</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.033937</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.050633</td>\n",
       "      <td>0.046875</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.178571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047144</td>\n",
       "      <td>0.042526</td>\n",
       "      <td>0.047144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>test</td>\n",
       "      <td>f1-score</td>\n",
       "      <td>0.038603</td>\n",
       "      <td>0.029821</td>\n",
       "      <td>0.044807</td>\n",
       "      <td>0.022161</td>\n",
       "      <td>0.023841</td>\n",
       "      <td>0.010204</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010152</td>\n",
       "      <td>0.016892</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003401</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047144</td>\n",
       "      <td>0.014139</td>\n",
       "      <td>0.080145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>test</td>\n",
       "      <td>support</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.047144</td>\n",
       "      <td>11836.000000</td>\n",
       "      <td>11836.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    split      index        I-ADR        E-ADR        B-ADR        S-ADR   \n",
       "0   train  precision     0.046754     0.036996     0.037456     0.017952  \\\n",
       "1   train     recall     0.047426     0.047633     0.050463     0.059428   \n",
       "2   train   f1-score     0.047088     0.041646     0.042997     0.027574   \n",
       "3   train    support  4449.000000  3506.000000  3349.000000  1363.000000   \n",
       "4     dev  precision     0.041051     0.042693     0.037829     0.014803   \n",
       "5     dev     recall     0.048356     0.057906     0.052036     0.043062   \n",
       "6     dev   f1-score     0.044405     0.049149     0.043810     0.022032   \n",
       "7     dev    support   517.000000   449.000000   442.000000   209.000000   \n",
       "8    test  precision     0.037300     0.026596     0.039007     0.014184   \n",
       "9    test     recall     0.040000     0.033937     0.052632     0.050633   \n",
       "10   test   f1-score     0.038603     0.029821     0.044807     0.022161   \n",
       "11   test    support   525.000000   442.000000   418.000000   158.000000   \n",
       "\n",
       "         S-Drug   B-Finding   E-Finding   I-Finding  ...      E-Drug   \n",
       "0      0.010419    0.002215    0.001994    0.001108  ...    0.000665  \\\n",
       "1      0.036547    0.050000    0.045000    0.033557  ...    0.027027   \n",
       "2      0.016215    0.004243    0.003818    0.002145  ...    0.001297   \n",
       "3   1286.000000  200.000000  200.000000  149.000000  ...  111.000000   \n",
       "4      0.011494    0.001642    0.001645    0.001645  ...    0.003295   \n",
       "5      0.042683    0.025641    0.029412    0.050000  ...    0.100000   \n",
       "6      0.018111    0.003086    0.003115    0.003185  ...    0.006380   \n",
       "7    164.000000   39.000000   34.000000   20.000000  ...   20.000000   \n",
       "8      0.015986    0.005319    0.000000    0.000000  ...    0.000000   \n",
       "9      0.046875    0.125000    0.000000    0.000000  ...    0.000000   \n",
       "10     0.023841    0.010204    0.000000    0.000000  ...    0.000000   \n",
       "11   192.000000   24.000000   27.000000    9.000000  ...   27.000000   \n",
       "\n",
       "     B-Symptom      B-Drug   S-Finding   I-Symptom     I-Drug  I-Disease   \n",
       "0     0.000886    0.001108    0.001108    0.000886   0.000222   0.000443  \\\n",
       "1     0.036364    0.045455    0.045872    0.038462   0.020000   0.040000   \n",
       "2     0.001730    0.002163    0.002163    0.001732   0.000438   0.000877   \n",
       "3   110.000000  110.000000  109.000000  104.000000  50.000000  50.000000   \n",
       "4     0.001642    0.001650    0.003289    0.003289   0.000000   0.000000   \n",
       "5     0.083333    0.050000    0.080000    0.086957   0.000000   0.000000   \n",
       "6     0.003221    0.003195    0.006319    0.006339   0.000000   0.000000   \n",
       "7    12.000000   20.000000   25.000000   23.000000   7.000000  13.000000   \n",
       "8     0.000000    0.005319    0.008865    0.000000   0.001776   0.000000   \n",
       "9     0.000000    0.111111    0.178571    0.000000   0.040000   0.000000   \n",
       "10    0.000000    0.010152    0.016892    0.000000   0.003401   0.000000   \n",
       "11   13.000000   27.000000   28.000000   20.000000  25.000000   3.000000   \n",
       "\n",
       "    accuracy     macro avg  weighted avg  \n",
       "0   0.047511      0.047514      0.697116  \n",
       "1   0.047511      0.047094      0.047511  \n",
       "2   0.047511      0.014366      0.080656  \n",
       "3   0.047511  94779.000000  94779.000000  \n",
       "4   0.047906      0.047991      0.709215  \n",
       "5   0.047906      0.041616      0.047906  \n",
       "6   0.047906      0.014557      0.081262  \n",
       "7   0.047906  12775.000000  12775.000000  \n",
       "8   0.047144      0.047117      0.696345  \n",
       "9   0.047144      0.042526      0.047144  \n",
       "10  0.047144      0.014139      0.080145  \n",
       "11  0.047144  11836.000000  11836.000000  \n",
       "\n",
       "[12 rows x 25 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>split</th>\n",
       "      <th>index</th>\n",
       "      <th>I-ADR</th>\n",
       "      <th>E-ADR</th>\n",
       "      <th>B-ADR</th>\n",
       "      <th>S-ADR</th>\n",
       "      <th>S-Drug</th>\n",
       "      <th>B-Finding</th>\n",
       "      <th>E-Finding</th>\n",
       "      <th>I-Finding</th>\n",
       "      <th>...</th>\n",
       "      <th>E-Drug</th>\n",
       "      <th>B-Symptom</th>\n",
       "      <th>B-Drug</th>\n",
       "      <th>S-Finding</th>\n",
       "      <th>I-Symptom</th>\n",
       "      <th>I-Drug</th>\n",
       "      <th>I-Disease</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>macro avg</th>\n",
       "      <th>weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train</td>\n",
       "      <td>precision</td>\n",
       "      <td>0.046941</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.046941</td>\n",
       "      <td>0.002235</td>\n",
       "      <td>0.002203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train</td>\n",
       "      <td>recall</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.046941</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.046941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train</td>\n",
       "      <td>f1-score</td>\n",
       "      <td>0.089672</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.046941</td>\n",
       "      <td>0.004270</td>\n",
       "      <td>0.004209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train</td>\n",
       "      <td>support</td>\n",
       "      <td>4449.000000</td>\n",
       "      <td>3506.0</td>\n",
       "      <td>3349.0</td>\n",
       "      <td>1363.0</td>\n",
       "      <td>1286.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>...</td>\n",
       "      <td>111.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.046941</td>\n",
       "      <td>94779.000000</td>\n",
       "      <td>94779.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dev</td>\n",
       "      <td>precision</td>\n",
       "      <td>0.040470</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.040470</td>\n",
       "      <td>0.001927</td>\n",
       "      <td>0.001638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dev</td>\n",
       "      <td>recall</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.040470</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.040470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dev</td>\n",
       "      <td>f1-score</td>\n",
       "      <td>0.077791</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.040470</td>\n",
       "      <td>0.003704</td>\n",
       "      <td>0.003148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>dev</td>\n",
       "      <td>support</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>449.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.040470</td>\n",
       "      <td>12775.000000</td>\n",
       "      <td>12775.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>test</td>\n",
       "      <td>precision</td>\n",
       "      <td>0.044356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044356</td>\n",
       "      <td>0.002112</td>\n",
       "      <td>0.001967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>test</td>\n",
       "      <td>recall</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044356</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.044356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>test</td>\n",
       "      <td>f1-score</td>\n",
       "      <td>0.084945</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044356</td>\n",
       "      <td>0.004045</td>\n",
       "      <td>0.003768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>test</td>\n",
       "      <td>support</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>442.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.044356</td>\n",
       "      <td>11836.000000</td>\n",
       "      <td>11836.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    split      index        I-ADR   E-ADR   B-ADR   S-ADR  S-Drug  B-Finding   \n",
       "0   train  precision     0.046941     0.0     0.0     0.0     0.0        0.0  \\\n",
       "1   train     recall     1.000000     0.0     0.0     0.0     0.0        0.0   \n",
       "2   train   f1-score     0.089672     0.0     0.0     0.0     0.0        0.0   \n",
       "3   train    support  4449.000000  3506.0  3349.0  1363.0  1286.0      200.0   \n",
       "4     dev  precision     0.040470     0.0     0.0     0.0     0.0        0.0   \n",
       "5     dev     recall     1.000000     0.0     0.0     0.0     0.0        0.0   \n",
       "6     dev   f1-score     0.077791     0.0     0.0     0.0     0.0        0.0   \n",
       "7     dev    support   517.000000   449.0   442.0   209.0   164.0       39.0   \n",
       "8    test  precision     0.044356     0.0     0.0     0.0     0.0        0.0   \n",
       "9    test     recall     1.000000     0.0     0.0     0.0     0.0        0.0   \n",
       "10   test   f1-score     0.084945     0.0     0.0     0.0     0.0        0.0   \n",
       "11   test    support   525.000000   442.0   418.0   158.0   192.0       24.0   \n",
       "\n",
       "    E-Finding  I-Finding  ...  E-Drug  B-Symptom  B-Drug  S-Finding   \n",
       "0         0.0        0.0  ...     0.0        0.0     0.0        0.0  \\\n",
       "1         0.0        0.0  ...     0.0        0.0     0.0        0.0   \n",
       "2         0.0        0.0  ...     0.0        0.0     0.0        0.0   \n",
       "3       200.0      149.0  ...   111.0      110.0   110.0      109.0   \n",
       "4         0.0        0.0  ...     0.0        0.0     0.0        0.0   \n",
       "5         0.0        0.0  ...     0.0        0.0     0.0        0.0   \n",
       "6         0.0        0.0  ...     0.0        0.0     0.0        0.0   \n",
       "7        34.0       20.0  ...    20.0       12.0    20.0       25.0   \n",
       "8         0.0        0.0  ...     0.0        0.0     0.0        0.0   \n",
       "9         0.0        0.0  ...     0.0        0.0     0.0        0.0   \n",
       "10        0.0        0.0  ...     0.0        0.0     0.0        0.0   \n",
       "11       27.0        9.0  ...    27.0       13.0    27.0       28.0   \n",
       "\n",
       "    I-Symptom  I-Drug  I-Disease  accuracy     macro avg  weighted avg  \n",
       "0         0.0     0.0        0.0  0.046941      0.002235      0.002203  \n",
       "1         0.0     0.0        0.0  0.046941      0.047619      0.046941  \n",
       "2         0.0     0.0        0.0  0.046941      0.004270      0.004209  \n",
       "3       104.0    50.0       50.0  0.046941  94779.000000  94779.000000  \n",
       "4         0.0     0.0        0.0  0.040470      0.001927      0.001638  \n",
       "5         0.0     0.0        0.0  0.040470      0.047619      0.040470  \n",
       "6         0.0     0.0        0.0  0.040470      0.003704      0.003148  \n",
       "7        23.0     7.0       13.0  0.040470  12775.000000  12775.000000  \n",
       "8         0.0     0.0        0.0  0.044356      0.002112      0.001967  \n",
       "9         0.0     0.0        0.0  0.044356      0.047619      0.044356  \n",
       "10        0.0     0.0        0.0  0.044356      0.004045      0.003768  \n",
       "11       20.0    25.0        3.0  0.044356  11836.000000  11836.000000  \n",
       "\n",
       "[12 rows x 25 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_mostfreq"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## meddra - random baseline"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## meddra - most frequent baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 948/948 [00:06<00:00, 140.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S-Lipitor\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 119/119 [00:00<00:00, 129.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S-Lipitor\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 119/119 [00:00<00:00, 121.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S-Lipitor\n"
     ]
    }
   ],
   "source": [
    "res_random = res_mostfreq = pd.DataFrame(columns=['split','index']+meddra_tags_ordered)\n",
    "train=None\n",
    "for split, split_name in [(train_docs,'train'),(dev_docs,'dev'),(test_docs,'test')]:\n",
    "    y_true = []\n",
    "    for doc in tqdm(split, total=len(split)):\n",
    "        for token in doc:\n",
    "            labels = get_all_labels(token)\n",
    "            y_true += [a.value for a in labels['meddra']]\n",
    "    \n",
    "    if split_name == 'train':\n",
    "        train = y_true\n",
    "    \n",
    "    np.random.seed(1234)\n",
    "    y_pred = random.sample(repeat_arr(sorted(meddra_tags_ordered+['O']), len(y_true)), len(y_true))\n",
    "    y_true1, y_pred1 = remove_o_tags(y_true, y_pred)\n",
    "    res = pd.DataFrame.from_dict(classification_report(y_true1, y_pred1, output_dict=True, zero_division=0)).reset_index()\n",
    "    temp = res['index']\n",
    "    res.drop('index',axis=1,inplace=True)\n",
    "    res.insert(loc=0, column='split', value=split_name)\n",
    "    res.insert(loc=1, column='index', value=temp)\n",
    "    res_random = pd.concat([res_random, res],ignore_index=True)\n",
    "    \n",
    "    mostfreq = meddra_tags_ordered[0]\n",
    "    print(mostfreq)\n",
    "    y_pred = [mostfreq]*len(y_true)\n",
    "    y_true1, y_pred1 = remove_o_tags(y_true, y_pred)\n",
    "    res = pd.DataFrame.from_dict(classification_report(y_true1, y_pred1, output_dict=True, zero_division=0)).reset_index()\n",
    "    temp = res['index']\n",
    "    res.drop('index',axis=1,inplace=True)\n",
    "    res.insert(loc=0, column='split', value=split_name)\n",
    "    res.insert(loc=1, column='index', value=temp)\n",
    "    res_mostfreq = pd.concat([res_mostfreq, res],ignore_index=True)\n",
    "\n",
    "if 'O' in res_random.columns:\n",
    "    res_random.drop('O',axis=1,inplace=True)\n",
    "if 'O' in res_mostfreq.columns:\n",
    "    res_mostfreq.drop('O',axis=1,inplace=True)\n",
    "\n",
    "res_random = res_random.iloc[:, :res_random.columns.get_loc('weighted avg')+1]\n",
    "res_mostfreq = res_mostfreq.iloc[:, :res_mostfreq.columns.get_loc('weighted avg')+1]\n",
    "\n",
    "res_random.to_csv('baseline_meddra_model'+suf+'/random_classification_report.csv',index=False)\n",
    "res_mostfreq.to_csv('baseline_meddra_model'+suf+'/mostfreq_classification_report.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['split',\n",
       " 'index',\n",
       " 'S-Lipitor',\n",
       " 'I-CONCEPT_LESS',\n",
       " 'S-Pain',\n",
       " 'I-Severe_pain',\n",
       " 'B-CONCEPT_LESS',\n",
       " 'E-CONCEPT_LESS',\n",
       " 'E-Myalgia',\n",
       " 'B-Myalgia',\n",
       " 'E-Severe_pain',\n",
       " 'B-Severe_pain',\n",
       " 'S-CONCEPT_LESS',\n",
       " 'B-Arthralgia',\n",
       " 'E-Arthralgia',\n",
       " 'I-Muscle_weakness',\n",
       " 'S-Depression',\n",
       " 'I-Myalgia',\n",
       " 'S-Fatigue',\n",
       " 'I-Walking_disability',\n",
       " 'I-Pins_and_needles',\n",
       " 'B-Cramp_in_lower_limb',\n",
       " 'E-Cramp_in_lower_limb',\n",
       " 'I-Poor_short-term_memory',\n",
       " 'I-Weakness_of_limb',\n",
       " 'S-Headache',\n",
       " 'B-Backache',\n",
       " 'E-Backache',\n",
       " 'B-Muscle_weakness',\n",
       " 'E-Muscle_weakness',\n",
       " 'I-Foot_pain',\n",
       " 'B-Pain_in_lower_limb',\n",
       " 'E-Pain_in_lower_limb',\n",
       " 'I-Hip_pain',\n",
       " 'B-Amnesia',\n",
       " 'E-Amnesia',\n",
       " 'I-Pain_in_lower_limb',\n",
       " 'B-Hip_pain',\n",
       " 'E-Hip_pain',\n",
       " 'S-Diarrhoea',\n",
       " 'S-Muscle_cramp',\n",
       " 'I-Shoulder_pain',\n",
       " 'B-Walking_disability',\n",
       " 'E-Walking_disability',\n",
       " 'B-Knee_pain',\n",
       " 'E-Knee_pain',\n",
       " 'I-Cramp_in_lower_limb',\n",
       " 'B-Shoulder_pain',\n",
       " 'E-Shoulder_pain',\n",
       " 'B-Muscle_cramp',\n",
       " 'E-Muscle_cramp',\n",
       " 'E-Stomach_ache',\n",
       " 'S-Excessive_upper_gastrointestinal_gas',\n",
       " 'I-Generalised_aches_and_pains',\n",
       " 'B-Stomach_ache',\n",
       " 'I-Mentally_dull',\n",
       " 'S-Dizziness',\n",
       " 'E-Excessive_upper_gastrointestinal_gas',\n",
       " 'S-Nausea',\n",
       " 'I-Unable_to_get_off_a_bed',\n",
       " 'B-Excessive_upper_gastrointestinal_gas',\n",
       " 'I-Knee_pain',\n",
       " 'E-Neck_pain',\n",
       " 'S-Asthenia',\n",
       " 'B-Foot_pain',\n",
       " 'E-Foot_pain',\n",
       " 'B-Neck_pain',\n",
       " 'I-Neck_pain',\n",
       " 'I-Muscle_atrophy',\n",
       " 'S-Insomnia',\n",
       " 'S-Arthrotec',\n",
       " 'I-Excessive_upper_gastrointestinal_gas',\n",
       " 'S-Arthritis',\n",
       " 'I-Influenza-like_illness',\n",
       " 'I-Backache',\n",
       " 'I-Low_back_pain',\n",
       " 'I-Rash',\n",
       " 'E-Weakness_of_limb',\n",
       " 'S-Zocor',\n",
       " 'I-Myalgia/myositis_-_lower_leg',\n",
       " 'I-Pain_in_calf',\n",
       " 'B-Myocardial_infarction',\n",
       " 'E-Myocardial_infarction',\n",
       " 'I-Unable_to_walk',\n",
       " 'I-Lightheadedness',\n",
       " 'I-Flushing',\n",
       " 'I-Numbness_of_lower_limb',\n",
       " 'E-Pins_and_needles',\n",
       " 'B-Asthenia',\n",
       " 'E-Asthenia',\n",
       " 'E-Muscle_atrophy',\n",
       " 'B-Weakness_of_limb',\n",
       " 'B-Loss_of_hair',\n",
       " 'E-Loss_of_hair',\n",
       " 'I-Arthralgia',\n",
       " 'S-Bloating_symptom',\n",
       " 'I-Pain_in_upper_limb',\n",
       " 'B-Pins_and_needles',\n",
       " 'B-Blurred_vision_-_hazy',\n",
       " 'E-Blurred_vision_-_hazy',\n",
       " 'I-Pain_provoked_by_movement',\n",
       " 'B-Generalised_aches_and_pains',\n",
       " 'E-Generalised_aches_and_pains',\n",
       " 'B-Poor_short-term_memory',\n",
       " 'E-Poor_short-term_memory',\n",
       " 'B-Mentally_dull',\n",
       " 'E-Mentally_dull',\n",
       " 'B-Muscle_atrophy',\n",
       " 'B-Low_back_pain',\n",
       " 'E-Low_back_pain',\n",
       " 'B-Headache',\n",
       " 'E-Headache',\n",
       " 'S-ubidecarenone',\n",
       " 'I-Loss_of_motivation',\n",
       " 'E-Memory_impairment',\n",
       " 'I-Muscle_fatigue',\n",
       " 'B-Fatigue',\n",
       " 'E-Fatigue',\n",
       " 'S-Constipation',\n",
       " 'I-Myalgia/myositis_-_shoulder',\n",
       " 'S-Anxiety',\n",
       " 'I-Stomach_ache',\n",
       " 'I-Thigh_pain',\n",
       " 'I-Bleeding_between_periods',\n",
       " 'B-Memory_impairment',\n",
       " 'I-Reduced_libido',\n",
       " 'B-Chest_pain',\n",
       " 'E-Chest_pain',\n",
       " 'I-Memory_impairment',\n",
       " 'B-Stomach_cramps',\n",
       " 'E-Stomach_cramps',\n",
       " 'I-Sensation_of_hot_and_cold',\n",
       " 'I-Difficulty_walking_up_stairs',\n",
       " 'S-Pravachol',\n",
       " 'S-Voltaren',\n",
       " 'B-Myopathy',\n",
       " 'E-Myopathy',\n",
       " 'I-Generalised_acute_body_pains',\n",
       " 'E-Charleyhorse',\n",
       " 'I-Unable_to_concentrate',\n",
       " 'I-Burning_feet',\n",
       " 'B-Unable_to_walk',\n",
       " 'E-Unable_to_walk',\n",
       " 'B-Influenza-like_illness',\n",
       " 'E-Influenza-like_illness',\n",
       " 'I-Muscle_cramp',\n",
       " 'I-Charleyhorse',\n",
       " 'B-Difficulty_sleeping',\n",
       " 'E-Difficulty_sleeping',\n",
       " 'B-Upset_stomach',\n",
       " 'E-Upset_stomach',\n",
       " 'B-Cannot_sleep_at_all',\n",
       " 'E-Cannot_sleep_at_all',\n",
       " 'I-Ankle_pain',\n",
       " 'I-Rupture_of_Achilles_tendon',\n",
       " 'I-Unable_to_climb_stairs',\n",
       " 'B-Abdominal_pain',\n",
       " 'E-Abdominal_pain',\n",
       " 'I-Unable_to_walk_up_step',\n",
       " 'I-Myalgia/myositis_-_multiple',\n",
       " 'I-Difficulty_sleeping',\n",
       " 'B-Visual_disturbance',\n",
       " 'E-Visual_disturbance',\n",
       " 'B-Pain_in_calf',\n",
       " 'E-Pain_in_calf',\n",
       " 'B-Weight_gain',\n",
       " 'E-Weight_gain',\n",
       " 'I-Thoracic_back_pain',\n",
       " 'I-Visual_disturbance',\n",
       " 'I-Cramp_in_calf',\n",
       " 'I-Headache',\n",
       " 'B-Increased_cholesterol_esters',\n",
       " 'E-Increased_cholesterol_esters',\n",
       " 'B-Heart_disease',\n",
       " 'E-Heart_disease',\n",
       " 'S-Pins_and_needles',\n",
       " 'S-Numbness',\n",
       " 'B-Lack_of_energy',\n",
       " 'E-Lack_of_energy',\n",
       " 'I-Acid_reflux',\n",
       " 'I-Postmenopausal_bleeding',\n",
       " 'I-Paraesthesia_of_lower_extremity',\n",
       " 'E-Muscle_fatigue',\n",
       " 'B-Tired',\n",
       " 'E-Tired',\n",
       " 'B-Reduced_libido',\n",
       " 'E-Reduced_libido',\n",
       " 'I-Tired',\n",
       " 'S-Vertigo',\n",
       " 'I-Serum_cholesterol_raised',\n",
       " 'I-Unable_to_move_arm',\n",
       " 'I-Arthritis_of_hand',\n",
       " 'B-Muscle_fatigue',\n",
       " 'B-Unable_to_concentrate',\n",
       " 'E-Unable_to_concentrate',\n",
       " 'I-Impairment_of_balance',\n",
       " 'I-Restless_legs',\n",
       " 'S-Swelling',\n",
       " 'B-Pain_in_upper_limb',\n",
       " 'E-Pain_in_upper_limb',\n",
       " 'I-Tired_all_the_time',\n",
       " 'E-Myalgia/myositis_-_lower_leg',\n",
       " 'S-Crestor',\n",
       " 'I-Myalgia/myositis_-pelvis/thigh',\n",
       " 'I-Burning_pain',\n",
       " 'S-Tiredness_symptom',\n",
       " 'S-Rash',\n",
       " 'E-Muscle_twitch',\n",
       " 'S-Feeling_irritable',\n",
       " 'I-Intercostal_myalgia',\n",
       " 'I-Failure_to_lose_weight',\n",
       " 'B-Myalgia/myositis_-_lower_leg',\n",
       " 'B-Pain',\n",
       " 'E-Pain',\n",
       " 'I-Pulse_fast',\n",
       " 'B-Dyspnoea',\n",
       " 'I-Dyspnoea',\n",
       " 'E-Dyspnoea',\n",
       " 'S-Itching',\n",
       " 'I-Stiff_legs',\n",
       " 'I-Tight_chest',\n",
       " 'I-Stabbing_pain',\n",
       " 'B-Impairment_of_balance',\n",
       " 'E-Impairment_of_balance',\n",
       " 'I-Cramp_in_foot',\n",
       " 'S-Tired',\n",
       " 'B-Ankle_pain',\n",
       " 'E-Ankle_pain',\n",
       " 'I-Irregular_menstruation',\n",
       " 'S-Diabetes_mellitus',\n",
       " 'S-Fibromyalgia',\n",
       " 'I-Liver_enzymes_abnormal',\n",
       " 'B-Vaginal_bleeding',\n",
       " 'E-Vaginal_bleeding',\n",
       " 'I-Myalgia/myositis_-_forearm',\n",
       " 'I-Unable_to_think_clearly',\n",
       " 'I-Pain',\n",
       " 'S-ezetimibe',\n",
       " 'I-Hypersomnia',\n",
       " 'I-Stomach_cramps',\n",
       " 'I-Finding_of_increased_blood_pressure',\n",
       " 'B-ascorbic_acid',\n",
       " 'E-ascorbic_acid',\n",
       " 'I-Itching_of_skin',\n",
       " 'I-Muscle_tension',\n",
       " 'S-Stiffness',\n",
       " 'I-Pain_provoked_by_walking',\n",
       " 'I-Uterine_spasm',\n",
       " 'I-Difficulty_getting_off_a_bed',\n",
       " 'I-Lack_of_energy',\n",
       " 'I-Limitation_of_joint_movement',\n",
       " 'I-Spasm_of_back_muscles',\n",
       " 'I-Tinnitus',\n",
       " 'I-Upset_stomach',\n",
       " 'I-Urinary_frequency',\n",
       " 'B-Cramp_in_calf',\n",
       " 'E-Cramp_in_calf',\n",
       " 'B-Loss_of_motivation',\n",
       " 'E-Loss_of_motivation',\n",
       " 'I-Numbness_of_limbs',\n",
       " 'B-Muscle_twitch',\n",
       " 'B-Lightheadedness',\n",
       " 'E-Lightheadedness',\n",
       " 'B-Liver_disease',\n",
       " 'E-Liver_disease',\n",
       " 'S-Flatulence/wind',\n",
       " 'B-Acid_reflux',\n",
       " 'E-Acid_reflux',\n",
       " 'S-Exhaustion',\n",
       " 'B-Pain_in_elbow',\n",
       " 'E-Pain_in_elbow',\n",
       " 'I-Generally_unwell',\n",
       " 'I-Myopathy',\n",
       " 'I-Irritable_bowel_syndrome_characterised_by_constipation',\n",
       " 'I-Aphasia',\n",
       " 'I-Tachycardia',\n",
       " 'B-Rash',\n",
       " 'E-Rash',\n",
       " 'B-Mood_swings',\n",
       " 'E-Mood_swings',\n",
       " 'I-Incoordination',\n",
       " 'I-Plantar_fasciitis',\n",
       " 'S-ibuprofen',\n",
       " 'B-Restless_legs',\n",
       " 'E-Restless_legs',\n",
       " 'B-Thigh_pain',\n",
       " 'E-Thigh_pain',\n",
       " 'S-Amyotrophic_lateral_sclerosis',\n",
       " 'S-Bleeding',\n",
       " 'B-Tired_all_the_time',\n",
       " 'E-Tired_all_the_time',\n",
       " 'S-fenofibrate',\n",
       " 'I-Random_blood_sugar_raised',\n",
       " 'I-Sharp_pain',\n",
       " 'E-Peripheral_neuropathy',\n",
       " 'I-Weakness_of_hand',\n",
       " 'B-Charleyhorse',\n",
       " 'I-Pain_in_left_arm',\n",
       " 'I-Deep_pain',\n",
       " 'I-Acute_pain',\n",
       " 'I-Muscle_twitch',\n",
       " 'S-Drowsy',\n",
       " 'S-Tendonitis',\n",
       " 'B-Unable_to_think_clearly',\n",
       " 'E-Unable_to_think_clearly',\n",
       " 'E-Burning_sensation',\n",
       " 'B-Abdominal_colic',\n",
       " 'E-Abdominal_colic',\n",
       " 'E-Cramp_in_foot',\n",
       " 'I-Cannot_sleep_at_all',\n",
       " 'B-Serum_creatinine_raised',\n",
       " 'E-Serum_creatinine_raised',\n",
       " 'I-Menorrhagia',\n",
       " 'B-Aptyalism',\n",
       " 'E-Aptyalism',\n",
       " 'B-Finding_of_increased_blood_pressure',\n",
       " 'E-Finding_of_increased_blood_pressure',\n",
       " 'I-Derealisation',\n",
       " 'E-Constant_pain',\n",
       " 'S-Indigestion',\n",
       " 'I-Blurred_vision_-_hazy',\n",
       " 'I-Numbness_of_upper_limb',\n",
       " 'I-Hand_pain',\n",
       " 'I-Loss_of_taste',\n",
       " 'I-Paraesthesia_of_hand',\n",
       " 'I-Unable_to_balance',\n",
       " 'I-Haematochezia',\n",
       " 'I-Difficulty_chewing',\n",
       " 'I-Burning_sensation',\n",
       " 'S-Vomiting',\n",
       " 'I-Foot_swelling',\n",
       " 'I-Fatigue',\n",
       " 'B-Cramp_in_foot',\n",
       " 'I-Migraine',\n",
       " 'B-Bleeding',\n",
       " 'E-Bleeding',\n",
       " 'E-Pain_provoked_by_movement',\n",
       " 'S-Gout',\n",
       " 'I-Discolouration_of_skin',\n",
       " 'B-Hand_pain',\n",
       " 'E-Hand_pain',\n",
       " 'S-Inflammatory_disorder',\n",
       " 'I-Myalgia/myositis_-_upper_arm',\n",
       " 'I-Loss_of_hair',\n",
       " 'I-Sensation_of_heaviness_in_limbs',\n",
       " 'B-Constant_pain',\n",
       " 'B-Serum_cholesterol_raised',\n",
       " 'E-Serum_cholesterol_raised',\n",
       " 'S-Tinnitus',\n",
       " 'I-Personality_change',\n",
       " 'I-Heavy_legs',\n",
       " 'I-Elbow_joint_pain',\n",
       " 'I-Haemorrhage_of_abdominal_cavity_structure',\n",
       " 'B-Peripheral_neuropathy',\n",
       " 'I-Arthralgia_of_the_lower_leg',\n",
       " 'S-Cerebrovascular_accident',\n",
       " 'S-Celebrex',\n",
       " 'I-Numbness_of_hand',\n",
       " 'I-Calf_muscle_weakness',\n",
       " 'I-Clicking_interphalangeal_joint_of_toe',\n",
       " 'B-Unable_to_get_off_a_bed',\n",
       " 'E-Unable_to_get_off_a_bed',\n",
       " 'B-Hypersomnia',\n",
       " 'E-Hypersomnia',\n",
       " 'B-Burning_sensation',\n",
       " 'S-Burning_sensation',\n",
       " 'B-Plantar_fasciitis',\n",
       " 'E-Plantar_fasciitis',\n",
       " 'B-Stiff_neck',\n",
       " 'E-Stiff_neck',\n",
       " 'S-Lightheadedness',\n",
       " 'B-Flatulence/wind',\n",
       " 'E-Flatulence/wind',\n",
       " 'B-Severe_depression',\n",
       " 'E-Severe_depression',\n",
       " 'S-Advil',\n",
       " 'B-Liver_enzymes_abnormal',\n",
       " 'E-Liver_enzymes_abnormal',\n",
       " 'B-Heel_pain',\n",
       " 'E-Heel_pain',\n",
       " \"I-Bell's_palsy\",\n",
       " 'I-Tightness_in_throat',\n",
       " 'I-Asthenia',\n",
       " 'B-Poor_concentration',\n",
       " 'E-Poor_concentration',\n",
       " 'S-simvastatin',\n",
       " 'I-Constant_pain',\n",
       " 'B-omega-3-acid_ethyl_esters-90',\n",
       " 'E-omega-3-acid_ethyl_esters-90',\n",
       " 'I-Facial_swelling',\n",
       " 'B-Joint_stiffness',\n",
       " 'E-Joint_stiffness',\n",
       " 'B-Increased_venous_pressure',\n",
       " 'I-Increased_venous_pressure',\n",
       " 'E-Increased_venous_pressure',\n",
       " 'S-Aleve',\n",
       " 'B-Excruciating_pain',\n",
       " 'E-Excruciating_pain',\n",
       " 'I-Impaired_exercise_tolerance',\n",
       " 'E-Impaired_exercise_tolerance',\n",
       " 'S-Neuropathy',\n",
       " 'I-Acenaesthesia',\n",
       " 'I-Ringing_in_ear',\n",
       " 'S-Stress',\n",
       " 'S-diclofenac',\n",
       " 'I-Unsteady_when_standing',\n",
       " 'I-Pain_in_left_leg',\n",
       " 'B-Difficulty_breathing',\n",
       " 'E-Difficulty_breathing',\n",
       " 'I-Hand_joint_stiff',\n",
       " 'B-Pain_in_toe',\n",
       " 'E-Pain_in_toe',\n",
       " 'I-Excruciating_pain',\n",
       " 'B-Menstrual_cramp',\n",
       " 'E-Menstrual_cramp',\n",
       " 'I-Heavy_feeling',\n",
       " 'B-Flushing',\n",
       " 'E-Flushing',\n",
       " 'I-Paraesthesia_of_foot',\n",
       " 'I-Ovarian_pain',\n",
       " 'I-Oedema_of_extremity',\n",
       " 'I-Weight_gain',\n",
       " 'I-Body_fluid_retention',\n",
       " 'I-Spasm',\n",
       " 'I-Peeling_of_skin',\n",
       " 'I-Oedema_of_lower_extremity',\n",
       " 'B-Burning_feet',\n",
       " 'E-Burning_feet',\n",
       " 'I-Cramp_in_limb',\n",
       " 'I-Feeling_empty',\n",
       " 'B-Kidney_disease',\n",
       " 'E-Kidney_disease',\n",
       " 'B-Myalgia/myositis_-_shoulder',\n",
       " 'E-Myalgia/myositis_-_shoulder',\n",
       " 'B-Generally_unwell',\n",
       " 'E-Generally_unwell',\n",
       " 'B-Rheumatoid_arthritis',\n",
       " 'E-Rheumatoid_arthritis',\n",
       " 'S-Lupus_erythematosus',\n",
       " 'S-Migraine',\n",
       " 'B-Pain_provoked_by_movement',\n",
       " 'S-Influenza',\n",
       " 'B-Vertigo',\n",
       " 'E-Vertigo',\n",
       " 'B-ubidecarenone',\n",
       " 'E-ubidecarenone',\n",
       " 'B-Lack_of_stamina',\n",
       " 'E-Lack_of_stamina',\n",
       " 'B-fish_oil_natural',\n",
       " 'E-fish_oil_natural',\n",
       " 'I-Unable_to_stand',\n",
       " 'B-Paraesthesia_of_hand',\n",
       " 'E-Paraesthesia_of_hand',\n",
       " 'B-Depression',\n",
       " 'E-Depression',\n",
       " 'B-Palpitations',\n",
       " 'E-Palpitations',\n",
       " 'S-Tremor',\n",
       " 'I-Heel_pain',\n",
       " 'I-White_blood_cell_count_abnormal',\n",
       " 'S-Moody',\n",
       " 'B-Impaired_exercise_tolerance',\n",
       " 'S-Delusional_disorder',\n",
       " 'I-Dizziness',\n",
       " 'S-Lethargy',\n",
       " 'B-Insomnia',\n",
       " 'E-Insomnia',\n",
       " 'S-Menopause_present',\n",
       " 'B-Random_blood_sugar_raised',\n",
       " 'E-Random_blood_sugar_raised',\n",
       " 'I-Pain_in_elbow',\n",
       " 'E-Stiff_legs',\n",
       " 'I-Feeling_faint',\n",
       " 'S-Illness',\n",
       " 'B-Unable_to_balance',\n",
       " 'E-Unable_to_balance',\n",
       " 'I-Malaise',\n",
       " 'I-Abdominal_pain',\n",
       " 'I-Lower_abdominal_pain',\n",
       " 'I-Right_hemiparesis',\n",
       " 'B-Swollen_ankle',\n",
       " 'E-Swollen_ankle',\n",
       " 'S-Multiple_sclerosis',\n",
       " 'I-Large_breast',\n",
       " 'I-Clicking_knee',\n",
       " 'I-Symptom_of_ankle',\n",
       " 'I-Initial_insomnia',\n",
       " 'I-Increased_lactic_acid_level',\n",
       " 'I-omega-3-acid_ethyl_esters-90',\n",
       " 'I-Loss_of_interest',\n",
       " 'I-Parkinsonian_tremor',\n",
       " 'I-Abdominal_hyperaesthesia',\n",
       " 'B-Tachycardia',\n",
       " 'E-Tachycardia',\n",
       " 'I-Erythema_of_skin',\n",
       " 'B-Pulse_fast',\n",
       " 'E-Pulse_fast',\n",
       " 'S-Cough',\n",
       " 'S-Sweating',\n",
       " 'B-Dizziness',\n",
       " 'E-Dizziness',\n",
       " 'B-Tylenol',\n",
       " 'E-Tylenol',\n",
       " 'I-Dry_skin',\n",
       " 'B-Muscle_rigidity',\n",
       " 'E-Muscle_rigidity',\n",
       " 'B-Menorrhagia',\n",
       " 'E-Menorrhagia',\n",
       " 'I-Lack_of_libido',\n",
       " 'S-Spasm',\n",
       " 'I-Lack_of_stamina',\n",
       " 'E-Muscle_tension',\n",
       " 'B-Unable_to_climb_stairs',\n",
       " 'E-Unable_to_climb_stairs',\n",
       " 'B-Weight_loss',\n",
       " 'E-Weight_loss',\n",
       " 'I-Swollen_legs',\n",
       " 'B-Uterine_spasm',\n",
       " 'E-Uterine_spasm',\n",
       " 'I-Renal_impairment',\n",
       " 'I-Gastrointestinal_tract_problem',\n",
       " 'E-Myalgia/myositis_-_forearm',\n",
       " 'B-Nausea',\n",
       " 'E-Nausea',\n",
       " 'S-Zoloft',\n",
       " 'I-Shoulder_joint_pain',\n",
       " 'E-Myalgia/myositis_-pelvis/thigh',\n",
       " 'B-Spasm_of_back_muscles',\n",
       " 'E-Spasm_of_back_muscles',\n",
       " 'B-Nerve_injury',\n",
       " 'E-Nerve_injury',\n",
       " 'B-Hypertension',\n",
       " 'E-Hypertension',\n",
       " 'I-Unable_to_get_out_of_a_chair',\n",
       " 'B-Slurred_speech',\n",
       " 'E-Slurred_speech',\n",
       " 'I-Knee_stiff',\n",
       " 'B-Malaise',\n",
       " 'E-Malaise',\n",
       " 'B-Panic_attack',\n",
       " 'E-Panic_attack',\n",
       " 'B-Stomach_problem',\n",
       " 'E-Stomach_problem',\n",
       " 'B-Paraesthesia_of_lower_extremity',\n",
       " 'E-Paraesthesia_of_lower_extremity',\n",
       " 'S-ezetimibe_+_simvastatin',\n",
       " 'E-Burning_pain',\n",
       " 'S-Confusion',\n",
       " 'S-Fever',\n",
       " 'I-Pain_in_toe',\n",
       " 'I-Weight_increased',\n",
       " 'B-Numbness_of_hand',\n",
       " 'E-Numbness_of_hand',\n",
       " 'E-Flatulence,_eructation_and_gas_pain',\n",
       " 'B-Numbness_of_lower_limb',\n",
       " 'E-Numbness_of_lower_limb',\n",
       " 'I-Unable_to_find_words',\n",
       " 'I-Unable_to_lift',\n",
       " 'I-Cramp_in_lower_leg',\n",
       " 'I-Tendonitis_AND/OR_tenosynovitis_of_the_elbow_region',\n",
       " 'I-Increased_liver_function',\n",
       " 'I-Unable_to_lie_down',\n",
       " 'I-Malignant_neoplasm_of_brain',\n",
       " 'I-Numbness_of_face',\n",
       " 'I-Tenalgia',\n",
       " 'I-Reduced_mobility',\n",
       " 'B-Spasm',\n",
       " 'E-Spasm',\n",
       " 'B-Dry_skin',\n",
       " 'I-Swelling_of_finger',\n",
       " 'E-Dry_skin',\n",
       " 'B-Numbness_of_foot',\n",
       " 'I-Numbness_of_foot',\n",
       " 'E-Numbness_of_foot',\n",
       " 'I-Glossitis',\n",
       " 'I-Unable_to_move_leg',\n",
       " 'B-cortisone',\n",
       " 'E-cortisone',\n",
       " 'B-alpha_tocopherol',\n",
       " 'E-alpha_tocopherol',\n",
       " 'B-Difficulty_swallowing',\n",
       " 'E-Difficulty_swallowing',\n",
       " 'I-Abdominal_colic',\n",
       " 'S-Malignant_neoplastic_disease',\n",
       " 'B-Tightness_in_throat',\n",
       " 'E-Tightness_in_throat',\n",
       " 'B-Renal_pain',\n",
       " 'E-Renal_pain',\n",
       " 'I-Non-smoker',\n",
       " 'S-Lopid',\n",
       " 'B-Spasmodic_movement',\n",
       " 'I-Spasmodic_movement',\n",
       " 'E-Spasmodic_movement',\n",
       " \"B-Bell's_palsy\",\n",
       " \"E-Bell's_palsy\",\n",
       " 'I-Unsteady_when_walking',\n",
       " 'B-Muscle_tension',\n",
       " 'B-Dry_eyes',\n",
       " 'E-Dry_eyes',\n",
       " 'I-Serum_creatinine_raised',\n",
       " 'B-Loss_of_appetite',\n",
       " 'I-Loss_of_appetite',\n",
       " 'E-Loss_of_appetite',\n",
       " 'I-Chest_pain',\n",
       " 'S-Forgetful',\n",
       " 'B-Pain_provoked_by_walking',\n",
       " 'E-Pain_provoked_by_walking',\n",
       " 'B-Facial_swelling',\n",
       " 'E-Facial_swelling',\n",
       " 'B-Rib_pain',\n",
       " 'E-Rib_pain',\n",
       " 'I-Urinary_retention',\n",
       " 'S-prednisone',\n",
       " 'I-Difficulty_lifting',\n",
       " 'S-Heartburn',\n",
       " 'B-Foot_swelling',\n",
       " 'E-Foot_swelling',\n",
       " 'S-Disorientated',\n",
       " 'S-Asthma',\n",
       " 'B-Tight_chest',\n",
       " 'E-Tight_chest',\n",
       " 'B-Thoracic_back_pain',\n",
       " 'E-Thoracic_back_pain',\n",
       " 'B-Limitation_of_joint_movement',\n",
       " 'E-Limitation_of_joint_movement',\n",
       " 'I-Liver_disease',\n",
       " 'B-Myalgia/myositis_-_forearm',\n",
       " 'B-Heavy_legs',\n",
       " 'E-Heavy_legs',\n",
       " 'B-Myalgia/myositis_-pelvis/thigh',\n",
       " 'B-Itching',\n",
       " 'E-Itching',\n",
       " 'I-Hypertension',\n",
       " 'I-Excessive_and_frequent_menstruation',\n",
       " 'B-Nasal_sinus_problem',\n",
       " 'E-Nasal_sinus_problem',\n",
       " 'B-Stiff_legs',\n",
       " 'B-Confusion',\n",
       " 'E-Confusion',\n",
       " 'B-Loss_of_taste',\n",
       " 'E-Loss_of_taste',\n",
       " 'B-Pain_in_finger',\n",
       " 'E-Pain_in_finger',\n",
       " 'B-Knee_stiff',\n",
       " 'E-Knee_stiff',\n",
       " 'B-Stiffness',\n",
       " 'E-Stiffness',\n",
       " 'I-Reduction_of_bulk_of_muscle',\n",
       " 'I-Respiratory_depression',\n",
       " 'S-Myalgia',\n",
       " 'B-Pain_in_throat',\n",
       " 'E-Pain_in_throat',\n",
       " 'I-Shoulder_stiff',\n",
       " 'B-Pain_in_wrist',\n",
       " 'E-Pain_in_wrist',\n",
       " 'B-Weight_increased',\n",
       " 'E-Weight_increased',\n",
       " 'E-Arthralgia_of_the_lower_leg',\n",
       " 'B-Stabbing_pain',\n",
       " 'E-Stabbing_pain',\n",
       " 'B-Type_2_diabetes_mellitus',\n",
       " 'E-Type_2_diabetes_mellitus',\n",
       " 'I-Impaired_mobility',\n",
       " 'I-Joint_crepitus',\n",
       " 'I-Dependance_on_walking_stick',\n",
       " 'I-Flatulence,_eructation_and_gas_pain',\n",
       " 'I-Poor_concentration',\n",
       " 'B-Cramp_in_lower_leg',\n",
       " 'E-Cramp_in_lower_leg',\n",
       " 'I-Difficulty_moving_hand',\n",
       " 'I-Acne',\n",
       " 'B-Bizarre_dreams',\n",
       " 'E-Bizarre_dreams',\n",
       " 'I-Night_pain',\n",
       " 'I-Peripheral_neuropathy',\n",
       " 'I-Discomfort',\n",
       " 'I-ubidecarenone',\n",
       " 'S-Norvasc',\n",
       " 'I-Hand_joint_pain',\n",
       " 'I-Difficulty_getting_out_of_a_chair',\n",
       " 'I-Blind_or_low_vision_-_one_eye_only',\n",
       " 'I-Always_sleepy',\n",
       " 'I-Amnesia',\n",
       " 'I-Tightness_sensation',\n",
       " 'I-Pain_provoked_by_rest',\n",
       " 'I-Difficulty_standing',\n",
       " 'B-Reduced_mobility',\n",
       " 'E-Reduced_mobility',\n",
       " 'B-Difficulty_walking_up_stairs',\n",
       " 'E-Difficulty_walking_up_stairs',\n",
       " 'B-Abdominal_bloating',\n",
       " 'E-Abdominal_bloating',\n",
       " 'S-Eczema',\n",
       " 'B-Swelling_of_finger',\n",
       " 'E-Swelling_of_finger',\n",
       " 'B-Hand_cramps',\n",
       " 'E-Hand_cramps',\n",
       " 'B-Carpal_tunnel_syndrome',\n",
       " 'E-Carpal_tunnel_syndrome',\n",
       " 'S-aspirin',\n",
       " 'S-Rhabdomyolysis',\n",
       " 'B-Infective_cystitis',\n",
       " 'E-Infective_cystitis',\n",
       " 'S-Syncope',\n",
       " 'B-Lack_of_libido',\n",
       " 'E-Lack_of_libido',\n",
       " 'S-Diovan',\n",
       " 'B-Suicidal_thoughts',\n",
       " 'E-Suicidal_thoughts',\n",
       " 'B-Migraine',\n",
       " 'E-Migraine',\n",
       " 'B-Muscle_strain',\n",
       " 'E-Muscle_strain',\n",
       " 'B-Steatosis_of_liver',\n",
       " 'E-Steatosis_of_liver',\n",
       " 'B-Itching_of_skin',\n",
       " 'E-Itching_of_skin',\n",
       " 'I-Vertigo',\n",
       " 'B-Sensation_of_heaviness_in_limbs',\n",
       " 'E-Sensation_of_heaviness_in_limbs',\n",
       " 'B-Abnormal_dreams',\n",
       " 'E-Abnormal_dreams',\n",
       " 'B-Swallowing_problem',\n",
       " 'E-Swallowing_problem',\n",
       " 'I-Hand_cramps',\n",
       " 'I-Serum_triglycerides_raised',\n",
       " 'B-Arthrotec',\n",
       " 'E-Arthrotec',\n",
       " 'B-Photosensitisation_due_to_sun',\n",
       " 'E-Photosensitisation_due_to_sun',\n",
       " 'B-Excessive_sweating',\n",
       " 'E-Excessive_sweating',\n",
       " 'I-Suicidal_thoughts',\n",
       " 'B-Cramp',\n",
       " 'E-Cramp',\n",
       " 'B-Swollen_legs',\n",
       " 'E-Swollen_legs',\n",
       " \"I-Parkinson's_disease\",\n",
       " 'I-Pounding_heart',\n",
       " 'B-Swollen_knee',\n",
       " 'E-Swollen_knee',\n",
       " 'B-Limping',\n",
       " 'E-Limping',\n",
       " 'B-Numbness_of_upper_limb',\n",
       " 'E-Numbness_of_upper_limb',\n",
       " 'I-Lumbar_facet_joint_pain',\n",
       " 'I-C-reactive_protein_abnormal',\n",
       " 'B-Elbow_joint_pain',\n",
       " 'E-Elbow_joint_pain',\n",
       " 'B-Ringing_in_ear',\n",
       " 'E-Ringing_in_ear',\n",
       " 'B-Increased_creatine_kinase_level',\n",
       " 'E-Increased_creatine_kinase_level',\n",
       " 'S-Bronchitis',\n",
       " 'B-Urine_looks_dark',\n",
       " 'E-Urine_looks_dark',\n",
       " 'I-Pain_in_finger',\n",
       " 'I-Numbness',\n",
       " 'I-Stiffness',\n",
       " 'B-Hunger_pain',\n",
       " 'E-Hunger_pain',\n",
       " 'B-Generalised_acute_body_pains',\n",
       " 'E-Generalised_acute_body_pains',\n",
       " 'B-Tinnitus',\n",
       " 'E-Tinnitus',\n",
       " 'B-Haematochezia',\n",
       " 'E-Haematochezia',\n",
       " 'B-Dysphonia',\n",
       " 'E-Dysphonia',\n",
       " 'I-Limping',\n",
       " 'B-Intervertebral_disc_prolapse',\n",
       " 'E-Intervertebral_disc_prolapse',\n",
       " 'B-Arthralgia_of_the_lower_leg',\n",
       " 'B-Hand_joint_stiff',\n",
       " 'E-Hand_joint_stiff',\n",
       " 'I-Abdominal_wind_pain',\n",
       " 'B-Gastric_ulcer',\n",
       " 'E-Gastric_ulcer',\n",
       " 'I-Difficulty_breathing',\n",
       " 'S-Malaise',\n",
       " 'I-Fine_motor_impairment',\n",
       " 'I-Arthritis_of_hip',\n",
       " 'I-Joint_stiffness',\n",
       " 'B-Flatulence,_eructation_and_gas_pain',\n",
       " 'S-Reflux',\n",
       " 'I-Urine:_dark/concentrated',\n",
       " 'I-Blistering_rash',\n",
       " 'I-Mucosal_numbness',\n",
       " 'B-Unable_to_move_arm',\n",
       " 'E-Unable_to_move_arm',\n",
       " 'I-Tingling_of_skin',\n",
       " 'S-Palpitations',\n",
       " 'I-Decreased_cholesterol_esters',\n",
       " 'I-Heavy_episode_of_vaginal_bleeding',\n",
       " 'B-Diarrhoea',\n",
       " 'E-Diarrhoea',\n",
       " 'I-Numbness_and_tingling_sensation_of_skin',\n",
       " 'S-Limping',\n",
       " 'I-Rupture_of_tendon_of_lower_leg_and_ankle',\n",
       " 'I-Hyperaesthesia',\n",
       " 'I-Impaired_bed_mobility',\n",
       " 'B-Malignant_neoplasm_of_brain',\n",
       " 'E-Malignant_neoplasm_of_brain',\n",
       " 'I-Coronary_arteriosclerosis',\n",
       " 'B-Tremor',\n",
       " 'I-Tremor',\n",
       " 'E-Tremor',\n",
       " 'S-Backache',\n",
       " 'B-Acute_pain',\n",
       " 'E-Acute_pain',\n",
       " 'I-Nerve_injury',\n",
       " 'I-Unable_to_move_all_four_limbs',\n",
       " 'I-Hyperaemia_of_surface_of_eye',\n",
       " 'I-Difficulty_dressing',\n",
       " 'I-Feels_everything_is_futile',\n",
       " 'B-Loose_stool',\n",
       " 'E-Loose_stool',\n",
       " 'I-Mood_swings',\n",
       " 'B-Peeling_of_skin',\n",
       " 'B-Incoordination',\n",
       " 'E-Incoordination',\n",
       " 'I-Propensity_to_adverse_reactions_to_drug',\n",
       " 'B-Numbness_of_limbs',\n",
       " 'E-Numbness_of_limbs',\n",
       " 'B-Stiff_limbs',\n",
       " 'I-Stiff_limbs',\n",
       " 'E-Stiff_limbs',\n",
       " 'I-Unable_to_walk_down_stairs',\n",
       " 'I-Chronic_back_pain',\n",
       " 'B-Arthritis',\n",
       " 'B-Myositis',\n",
       " 'I-Arthritis',\n",
       " 'I-Myositis',\n",
       " 'E-Myositis',\n",
       " 'E-Arthritis',\n",
       " 'B-Disorder_of_foot',\n",
       " 'E-Disorder_of_foot',\n",
       " 'B-Cramp_in_limb',\n",
       " 'E-Cramp_in_limb',\n",
       " 'S-Spasmodic_movement',\n",
       " 'B-Moody',\n",
       " 'E-Moody',\n",
       " 'B-Increased_appetite',\n",
       " 'E-Increased_appetite',\n",
       " 'B-Feeling_empty',\n",
       " 'E-Feeling_empty',\n",
       " 'B-Irregular_menstruation',\n",
       " 'E-Irregular_menstruation',\n",
       " 'S-Hypertension',\n",
       " 'S-Nightmares',\n",
       " 'S-metoprolol',\n",
       " 'B-Body_fluid_retention',\n",
       " 'E-Body_fluid_retention',\n",
       " 'S-Blister',\n",
       " 'I-Forgetful',\n",
       " 'I-Urinary_tract_infection',\n",
       " 'B-Bone_pain',\n",
       " 'E-Bone_pain',\n",
       " 'B-Digestive_symptom',\n",
       " 'E-Digestive_symptom',\n",
       " 'B-Scalp_itchy',\n",
       " 'E-Scalp_itchy',\n",
       " 'B-Myalgia/myositis_-_upper_arm',\n",
       " 'E-Myalgia/myositis_-_upper_arm',\n",
       " 'B-Exhaustion',\n",
       " 'E-Exhaustion',\n",
       " 'S-Feeling_unhappy',\n",
       " 'I-Dry_eyes',\n",
       " 'I-Right_upper_quadrant_pain',\n",
       " 'I-Liver_function_tests_abnormal',\n",
       " 'B-Serum_triglycerides_raised',\n",
       " 'E-Serum_triglycerides_raised',\n",
       " 'I-Deteriorating_renal_function',\n",
       " 'S-Lipex',\n",
       " 'S-Itching_of_skin',\n",
       " 'B-Hoarse',\n",
       " 'E-Hoarse',\n",
       " 'S-misoprostol',\n",
       " 'B-Cold_sweat',\n",
       " 'E-Cold_sweat',\n",
       " 'B-Renal_impairment',\n",
       " 'E-Renal_impairment',\n",
       " \"B-Parkinson's_disease\",\n",
       " \"E-Parkinson's_disease\",\n",
       " 'S-Neurontin',\n",
       " 'I-Neuroma_of_foot',\n",
       " 'B-Swelling_of_knee_joint',\n",
       " 'E-Swelling_of_knee_joint',\n",
       " 'B-Difficulty_getting_off_a_bed',\n",
       " 'E-Difficulty_getting_off_a_bed',\n",
       " 'S-Chill',\n",
       " 'B-Taste_sense_altered',\n",
       " 'E-Taste_sense_altered',\n",
       " 'B-Personality_change',\n",
       " 'E-Personality_change',\n",
       " 'B-Sleep_deprivation',\n",
       " 'I-Sleep_deprivation',\n",
       " 'E-Sleep_deprivation',\n",
       " 'I-Exhaustion',\n",
       " 'S-Sciatica',\n",
       " 'B-Shoulder_joint_pain',\n",
       " 'E-Shoulder_joint_pain',\n",
       " 'I-Fear',\n",
       " 'I-Disorder_of_nasal_sinus',\n",
       " 'I-Pain_in_limb',\n",
       " 'B-Sensation_of_hot_and_cold',\n",
       " 'E-Sensation_of_hot_and_cold',\n",
       " 'S-Endometriosis',\n",
       " 'B-Renal_failure',\n",
       " 'E-Renal_failure',\n",
       " 'I-Cold_sweat',\n",
       " 'B-Bleeding_between_periods',\n",
       " 'E-Bleeding_between_periods',\n",
       " 'B-Sharp_pain',\n",
       " 'E-Sharp_pain',\n",
       " 'B-Cough',\n",
       " 'E-Cough',\n",
       " 'B-Numbness',\n",
       " 'E-Numbness',\n",
       " 'B-Disorder_of_eye',\n",
       " 'E-Disorder_of_eye',\n",
       " 'I-Hip_stiff',\n",
       " 'S-Discomfort',\n",
       " 'I-Slow_on_legs',\n",
       " 'I-Unable_to_stand_up',\n",
       " 'I-Weight_loss',\n",
       " 'B-Rectal_haemorrhage',\n",
       " 'E-Rectal_haemorrhage',\n",
       " 'S-Colitis',\n",
       " 'S-atorvastatin',\n",
       " 'I-Dysphonia',\n",
       " 'I-Unable_to_eat',\n",
       " 'B-Burning_pain',\n",
       " 'S-Hot_sweats',\n",
       " 'B-Sinusitis',\n",
       " 'E-Sinusitis',\n",
       " 'S-Respiratory_tract_congestion',\n",
       " 'B-Bipolar_disorder',\n",
       " 'E-Bipolar_disorder',\n",
       " 'S-Osteoarthritis',\n",
       " 'I-Oedema_of_finger',\n",
       " 'S-Menstrual_spotting',\n",
       " 'E-Low_blood_pressure_reading',\n",
       " 'B-Raised_low_density_lipoprotein_cholesterol',\n",
       " 'E-Raised_low_density_lipoprotein_cholesterol',\n",
       " 'S-potassium',\n",
       " 'B-Always_hungry',\n",
       " 'E-Always_hungry',\n",
       " 'I-Joint_laxity',\n",
       " 'I-Lacks_confidence',\n",
       " 'B-Lateral_epicondylitis',\n",
       " 'E-Lateral_epicondylitis',\n",
       " 'B-Hypertonia',\n",
       " 'E-Hypertonia',\n",
       " 'I-Arthritis_of_knee',\n",
       " 'I-Type_II_diabetes_mellitus_uncontrolled',\n",
       " 'S-morphine',\n",
       " 'I-Type_2_diabetes_mellitus',\n",
       " 'B-Chronic_fatigue_syndrome',\n",
       " 'E-Chronic_fatigue_syndrome',\n",
       " 'B-Feeling_irritable',\n",
       " 'E-Feeling_irritable',\n",
       " 'I-Aptyalism',\n",
       " 'I-Influenza-like_symptoms',\n",
       " 'B-Lower_abdominal_pain',\n",
       " 'E-Lower_abdominal_pain',\n",
       " 'I-Increased_thirst',\n",
       " 'B-Heartburn',\n",
       " 'E-Heartburn',\n",
       " 'B-Numbness_of_face',\n",
       " 'E-Numbness_of_face',\n",
       " 'B-Urine:_dark/concentrated',\n",
       " 'E-Urine:_dark/concentrated',\n",
       " 'I-Swollen_nasal_mucosa',\n",
       " 'B-Loss_of_interest',\n",
       " 'E-Loss_of_interest',\n",
       " 'B-Weakness_of_hand',\n",
       " 'E-Weakness_of_hand',\n",
       " 'E-Blistering_rash',\n",
       " 'I-fish_oil_natural',\n",
       " 'I-Arthralgia_of_multiple_joints',\n",
       " 'I-Decreased_muscle_tone',\n",
       " 'I-Wrist_joint_pain',\n",
       " 'I-Middle_insomnia',\n",
       " 'I-Swelling_of_knee_joint',\n",
       " 'I-Hypertonia',\n",
       " 'B-Tingling_of_skin',\n",
       " 'E-Tingling_of_skin',\n",
       " 'B-Achilles_tendonitis',\n",
       " 'E-Achilles_tendonitis',\n",
       " 'B-Arthritis_of_hand',\n",
       " 'E-Arthritis_of_hand',\n",
       " 'I-Swollen_ankle',\n",
       " 'I-Loss_of_confidence',\n",
       " 'I-Contusion_of_hand',\n",
       " 'I-Shoulder_tendonitis',\n",
       " 'S-Disorder_of_muscle',\n",
       " 'I-Heartburn',\n",
       " 'S-codeine',\n",
       " 'B-Increased_liver_function',\n",
       " ...]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(res_random.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>split</th>\n",
       "      <th>index</th>\n",
       "      <th>S-Lipitor</th>\n",
       "      <th>I-CONCEPT_LESS</th>\n",
       "      <th>S-Pain</th>\n",
       "      <th>I-Severe_pain</th>\n",
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       "      <th>E-CONCEPT_LESS</th>\n",
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       "      <th>E-Feels_everything_is_futile</th>\n",
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       "      <th>I-Transient_global_amnesia</th>\n",
       "      <th>E-Transient_global_amnesia</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>macro avg</th>\n",
       "      <th>weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>f1-score</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train</td>\n",
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       "      <td>825.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>205.0</td>\n",
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       "      <td>9.551500e+04</td>\n",
       "      <td>95515.000000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dev</td>\n",
       "      <td>precision</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>test</td>\n",
       "      <td>f1-score</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.000335</td>\n",
       "      <td>3.136733e-07</td>\n",
       "      <td>0.000669</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
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       "      <td>57.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.194800e+04</td>\n",
       "      <td>11948.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 2455 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    split      index  S-Lipitor  I-CONCEPT_LESS  S-Pain  I-Severe_pain   \n",
       "0   train  precision        0.0             0.0     0.0            0.0  \\\n",
       "1   train     recall        0.0             0.0     0.0            0.0   \n",
       "2   train   f1-score        0.0             0.0     0.0            0.0   \n",
       "3   train    support      825.0           514.0   275.0          265.0   \n",
       "4     dev  precision        0.0             0.0     0.0            0.0   \n",
       "5     dev     recall        0.0             0.0     0.0            0.0   \n",
       "6     dev   f1-score        0.0             0.0     0.0            0.0   \n",
       "7     dev    support      120.0            63.0    44.0           19.0   \n",
       "8    test  precision        0.0             0.0     0.0            0.0   \n",
       "9    test     recall        0.0             0.0     0.0            0.0   \n",
       "10   test   f1-score        0.0             0.0     0.0            0.0   \n",
       "11   test    support      125.0            57.0    34.0           22.0   \n",
       "\n",
       "    B-CONCEPT_LESS  E-CONCEPT_LESS  E-Myalgia  B-Myalgia  ...   \n",
       "0              0.0             0.0        0.0        0.0  ...  \\\n",
       "1              0.0             0.0        0.0        0.0  ...   \n",
       "2              0.0             0.0        0.0        0.0  ...   \n",
       "3            232.0           232.0      209.0      205.0  ...   \n",
       "4              0.0             0.0        0.0        0.0  ...   \n",
       "5              0.0             0.0        0.0        0.0  ...   \n",
       "6              0.0             0.0        0.0        0.0  ...   \n",
       "7             33.0            33.0       26.0       26.0  ...   \n",
       "8              0.0             0.0        0.0        0.0  ...   \n",
       "9              0.0             0.0        0.0        0.0  ...   \n",
       "10             0.0             0.0        0.0        0.0  ...   \n",
       "11            29.0            29.0       24.0       24.0  ...   \n",
       "\n",
       "    B-Morbid_thoughts  E-Morbid_thoughts  B-Feels_everything_is_futile   \n",
       "0                 0.0                0.0                           0.0  \\\n",
       "1                 0.0                0.0                           0.0   \n",
       "2                 0.0                0.0                           0.0   \n",
       "3                 1.0                1.0                           1.0   \n",
       "4                 0.0                0.0                           0.0   \n",
       "5                 0.0                0.0                           0.0   \n",
       "6                 0.0                0.0                           0.0   \n",
       "7                 0.0                0.0                           0.0   \n",
       "8                 0.0                0.0                           0.0   \n",
       "9                 0.0                0.0                           0.0   \n",
       "10                0.0                0.0                           0.0   \n",
       "11                0.0                0.0                           0.0   \n",
       "\n",
       "    E-Feels_everything_is_futile  B-Transient_global_amnesia   \n",
       "0                            0.0                         0.0  \\\n",
       "1                            0.0                         0.0   \n",
       "2                            0.0                         0.0   \n",
       "3                            1.0                         1.0   \n",
       "4                            0.0                         0.0   \n",
       "5                            0.0                         0.0   \n",
       "6                            0.0                         0.0   \n",
       "7                            0.0                         0.0   \n",
       "8                            0.0                         0.0   \n",
       "9                            0.0                         0.0   \n",
       "10                           0.0                         0.0   \n",
       "11                           0.0                         0.0   \n",
       "\n",
       "    I-Transient_global_amnesia  E-Transient_global_amnesia  accuracy   \n",
       "0                          0.0                         0.0  0.000377  \\\n",
       "1                          0.0                         0.0  0.000377   \n",
       "2                          0.0                         0.0  0.000377   \n",
       "3                          1.0                         1.0  0.000377   \n",
       "4                          0.0                         0.0  0.000622   \n",
       "5                          0.0                         0.0  0.000622   \n",
       "6                          0.0                         0.0  0.000622   \n",
       "7                          0.0                         0.0  0.000622   \n",
       "8                          0.0                         0.0  0.000335   \n",
       "9                          0.0                         0.0  0.000335   \n",
       "10                         0.0                         0.0  0.000335   \n",
       "11                         0.0                         0.0  0.000335   \n",
       "\n",
       "       macro avg  weighted avg  \n",
       "0   3.766124e-04      0.636741  \n",
       "1   8.403350e-05      0.000377  \n",
       "2   6.987041e-05      0.000684  \n",
       "3   9.551500e+04  95515.000000  \n",
       "4   6.091239e-04      0.832755  \n",
       "5   8.020867e-04      0.000622  \n",
       "6   2.780790e-04      0.000944  \n",
       "7   1.286400e+04  12864.000000  \n",
       "8   3.862495e-04      0.824155  \n",
       "9   1.569004e-07      0.000335  \n",
       "10  3.136733e-07      0.000669  \n",
       "11  1.194800e+04  11948.000000  \n",
       "\n",
       "[12 rows x 2455 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>split</th>\n",
       "      <th>index</th>\n",
       "      <th>S-Lipitor</th>\n",
       "      <th>I-CONCEPT_LESS</th>\n",
       "      <th>S-Pain</th>\n",
       "      <th>I-Severe_pain</th>\n",
       "      <th>B-CONCEPT_LESS</th>\n",
       "      <th>E-CONCEPT_LESS</th>\n",
       "      <th>E-Myalgia</th>\n",
       "      <th>B-Myalgia</th>\n",
       "      <th>...</th>\n",
       "      <th>B-Morbid_thoughts</th>\n",
       "      <th>E-Morbid_thoughts</th>\n",
       "      <th>B-Feels_everything_is_futile</th>\n",
       "      <th>E-Feels_everything_is_futile</th>\n",
       "      <th>B-Transient_global_amnesia</th>\n",
       "      <th>I-Transient_global_amnesia</th>\n",
       "      <th>E-Transient_global_amnesia</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>macro avg</th>\n",
       "      <th>weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train</td>\n",
       "      <td>precision</td>\n",
       "      <td>0.008637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008637</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train</td>\n",
       "      <td>recall</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008637</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.008637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train</td>\n",
       "      <td>f1-score</td>\n",
       "      <td>0.017127</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008637</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train</td>\n",
       "      <td>support</td>\n",
       "      <td>825.000000</td>\n",
       "      <td>514.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008637</td>\n",
       "      <td>95515.000000</td>\n",
       "      <td>95515.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dev</td>\n",
       "      <td>precision</td>\n",
       "      <td>0.009328</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.009328</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dev</td>\n",
       "      <td>recall</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.009328</td>\n",
       "      <td>0.001401</td>\n",
       "      <td>0.009328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dev</td>\n",
       "      <td>f1-score</td>\n",
       "      <td>0.018484</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.009328</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>dev</td>\n",
       "      <td>support</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>63.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.009328</td>\n",
       "      <td>12864.000000</td>\n",
       "      <td>12864.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>test</td>\n",
       "      <td>precision</td>\n",
       "      <td>0.010462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010462</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>test</td>\n",
       "      <td>recall</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010462</td>\n",
       "      <td>0.001387</td>\n",
       "      <td>0.010462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>test</td>\n",
       "      <td>f1-score</td>\n",
       "      <td>0.020707</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010462</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>test</td>\n",
       "      <td>support</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>57.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010462</td>\n",
       "      <td>11948.000000</td>\n",
       "      <td>11948.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 2455 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    split      index   S-Lipitor  I-CONCEPT_LESS  S-Pain  I-Severe_pain   \n",
       "0   train  precision    0.008637             0.0     0.0            0.0  \\\n",
       "1   train     recall    1.000000             0.0     0.0            0.0   \n",
       "2   train   f1-score    0.017127             0.0     0.0            0.0   \n",
       "3   train    support  825.000000           514.0   275.0          265.0   \n",
       "4     dev  precision    0.009328             0.0     0.0            0.0   \n",
       "5     dev     recall    1.000000             0.0     0.0            0.0   \n",
       "6     dev   f1-score    0.018484             0.0     0.0            0.0   \n",
       "7     dev    support  120.000000            63.0    44.0           19.0   \n",
       "8    test  precision    0.010462             0.0     0.0            0.0   \n",
       "9    test     recall    1.000000             0.0     0.0            0.0   \n",
       "10   test   f1-score    0.020707             0.0     0.0            0.0   \n",
       "11   test    support  125.000000            57.0    34.0           22.0   \n",
       "\n",
       "    B-CONCEPT_LESS  E-CONCEPT_LESS  E-Myalgia  B-Myalgia  ...   \n",
       "0              0.0             0.0        0.0        0.0  ...  \\\n",
       "1              0.0             0.0        0.0        0.0  ...   \n",
       "2              0.0             0.0        0.0        0.0  ...   \n",
       "3            232.0           232.0      209.0      205.0  ...   \n",
       "4              0.0             0.0        0.0        0.0  ...   \n",
       "5              0.0             0.0        0.0        0.0  ...   \n",
       "6              0.0             0.0        0.0        0.0  ...   \n",
       "7             33.0            33.0       26.0       26.0  ...   \n",
       "8              0.0             0.0        0.0        0.0  ...   \n",
       "9              0.0             0.0        0.0        0.0  ...   \n",
       "10             0.0             0.0        0.0        0.0  ...   \n",
       "11            29.0            29.0       24.0       24.0  ...   \n",
       "\n",
       "    B-Morbid_thoughts  E-Morbid_thoughts  B-Feels_everything_is_futile   \n",
       "0                 0.0                0.0                           0.0  \\\n",
       "1                 0.0                0.0                           0.0   \n",
       "2                 0.0                0.0                           0.0   \n",
       "3                 1.0                1.0                           1.0   \n",
       "4                 NaN                NaN                           NaN   \n",
       "5                 NaN                NaN                           NaN   \n",
       "6                 NaN                NaN                           NaN   \n",
       "7                 NaN                NaN                           NaN   \n",
       "8                 NaN                NaN                           NaN   \n",
       "9                 NaN                NaN                           NaN   \n",
       "10                NaN                NaN                           NaN   \n",
       "11                NaN                NaN                           NaN   \n",
       "\n",
       "    E-Feels_everything_is_futile  B-Transient_global_amnesia   \n",
       "0                            0.0                         0.0  \\\n",
       "1                            0.0                         0.0   \n",
       "2                            0.0                         0.0   \n",
       "3                            1.0                         1.0   \n",
       "4                            NaN                         NaN   \n",
       "5                            NaN                         NaN   \n",
       "6                            NaN                         NaN   \n",
       "7                            NaN                         NaN   \n",
       "8                            NaN                         NaN   \n",
       "9                            NaN                         NaN   \n",
       "10                           NaN                         NaN   \n",
       "11                           NaN                         NaN   \n",
       "\n",
       "    I-Transient_global_amnesia  E-Transient_global_amnesia  accuracy   \n",
       "0                          0.0                         0.0  0.008637  \\\n",
       "1                          0.0                         0.0  0.008637   \n",
       "2                          0.0                         0.0  0.008637   \n",
       "3                          1.0                         1.0  0.008637   \n",
       "4                          NaN                         NaN  0.009328   \n",
       "5                          NaN                         NaN  0.009328   \n",
       "6                          NaN                         NaN  0.009328   \n",
       "7                          NaN                         NaN  0.009328   \n",
       "8                          NaN                         NaN  0.010462   \n",
       "9                          NaN                         NaN  0.010462   \n",
       "10                         NaN                         NaN  0.010462   \n",
       "11                         NaN                         NaN  0.010462   \n",
       "\n",
       "       macro avg  weighted avg  \n",
       "0       0.000004      0.000075  \n",
       "1       0.000408      0.008637  \n",
       "2       0.000007      0.000148  \n",
       "3   95515.000000  95515.000000  \n",
       "4       0.000013      0.000087  \n",
       "5       0.001401      0.009328  \n",
       "6       0.000026      0.000172  \n",
       "7   12864.000000  12864.000000  \n",
       "8       0.000015      0.000109  \n",
       "9       0.001387      0.010462  \n",
       "10      0.000029      0.000217  \n",
       "11  11948.000000  11948.000000  \n",
       "\n",
       "[12 rows x 2455 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "res_mostfreq"
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