ClinicalTrialLLM-Extractor

A tool for extracting and analyzing clinical trial data using Large Language Models.

Overview

This project extracts structured information from clinical trials using LLMs, focusing on key parameters and outcomes. The pipeline includes data acquisition, filtering, LLM-based information extraction, and statistical analysis.

Installation

git clone https://github.com/yourusername/ClinicalTrialLLM-Extractor.git
cd ClinicalTrialLLM-Extractor
pip install -r requirements.txt

Project Structure

• data/ - Raw and processed datasets
  • raw/ - Original clinical trial data
  • filtered/ - Filtered datasets
  • processed/ - LLM-processed data
• notebooks/ - Jupyter notebooks for pipeline stages
• figures/ - Generated visualizations
• results/ - Final analysis outputs

Usage

1. Data acquisition: jupyter notebook notebooks/01_dataset_pull.ipynb
2. Data filtering: jupyter notebook notebooks/02_simple_filtering.ipynb
3. LLM processing: jupyter notebook notebooks/03_LLM_processor.ipynb
4. Analysis: jupyter notebook notebooks/paper_stats.ipynb

License

This project is licensed under the MIT License - see the LICENSE file for details.