From Text to Tables: A Local Privacy Preserving Large Language Model for Structured Information Retrieval from Medical Documents

Note: This documentation is currently under construction. Some sections may be updated or changed as development progresses.

General Setup Instructions

Before running the scripts, please ensure the following setup steps are completed:

1. Python Installation: Make sure Python is installed on your system. The scripts are compatible with Python 3.8.
2. Dependency Installation: Install the required Python packages. You can do this easily by using the requirements.txt file provided:

   pip install -r requirements.txt

Data Preparation

Place your dataset files in accessible paths on your system.

Script-Specific Instructions

MIMIC Features Extraction Script (extract_mimic_features_from_report.py)

This Python script extracts and analyzes specific medical features from patient reports using a predefined grammar and prompt.

Usage

Run the script from the command line by specifying the path to your MIMIC ground truth data:

python extract_mimic_features_from_report.py path/to/MIMIC_groundtruth.csv

Confusion Matrix Analysis Script (confusionmatrix.py)

This Python script generates confusion matrices for machine learning model predictions, comparing predictions against a ground truth dataset to visualize the performance of a classification model.

Usage

Run the script from the command line by specifying the path to your ground truth data and predictions:

python confusionmatrix.py path/to/ground_truth.csv path/to/predictions.jsonl

Accuracy Comparison Script (accuracy_comparison.py)

This Python script compares the accuracy of different machine learning models, calculating and visualizing the accuracy of each model for various symptoms.

Usage

Run the script from the command line with the path to your ground truth data:

python accuracy_comparison.py path/to/ground_truth.csv