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UC-Berkeley-I-School · Apr 13, 2024 · 5b85dcb · 5b85dcb
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diff --git a/pages/about.mdx b/pages/about.mdx
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 This is the capstone project for the MIDS program at UC Berkeley. The goal of this project is to develop a machine learning model
 that can predict the likelihood of a patient having a certain disease based on various textual, tabular, and visual data.
 
-In medical diagnostics, healthcare professionals encounter significant challenges due to the fragmented and varied formats of patient data. These issues often lead to longer times for diagnosing, higher healthcare costs, and treatment delays, all of which increase risks for patients.
+In medical diagnostics, healthcare professionals encounter significant challenges due to the fragmented and varied formats of patient data. These issues often lead to longer times for diagnosing, higher healthcare costs, and treatment delays, which collectively heighten risks to patients.
 
 ### Untapped Potential of EHR Systems
 Despite the detailed patient profiles created by Electronic Health Record (EHR) systems, which include diagnostic results, radiology studies, and clinical notes, the full potential of these records for personalized patient care remains largely untapped. Current AI diagnostic tools are unable to fully leverage the diverse data types available, leading to significant gaps in the ability of healthcare providers to analyze and understand the complexities of healthcare data. This oversight impedes the advancement of personalized medicine and comprehensive patient care.

diff --git a/pages/docs/data.mdx b/pages/docs/data.mdx
@@ -17,10 +17,10 @@ The following describes the steps taken to preprocess and consolidate various da
 
 - Chest XRay Encoded Pathologies
 
-  - Pathology Encoding: Processed the `mimic-cxr-2.0.0-chexpert.csv` to encode pathologies for each patient, retaining only records with one or more pathologies.
+  - Pathology Encoding: Processed the patholigies categories to encode pathologies for each patient, retaining only records with one or more pathologies.
 
 - Chest XRay Metadata
-  - Deduplication: Removed duplicate instances within the `study.csv` to ensure unique study records.
+  - Deduplication: Removed duplicate instances within the patient studies to ensure unique study records.
 
 ## Dataset Merging