Generative AI for Clinical Trial Data (Internet Search + Tabular Data)

This repository contains a specialized application designed to search, parse, and provide insights into the latest clinical trial data from the FDA, NIH, and CDC databases.

Motivation

The field of medical research is rapidly advancing, with numerous clinical trials generating vast amounts of data every day. However, keeping up with this deluge of information is a challenging task for researchers, healthcare professionals, and patients alike. This project aims to harness the power of Generative AI, specifically Langchain, to streamline the process of accessing, understanding, and analyzing clinical trial data. By integrating data from key sources like the FDA, NIH, and CDC, this tool provides an efficient and user-friendly platform for gaining insights into the latest developments in medical research.

Features

Automated Data Retrieval: Utilizes advanced scraping and parsing techniques to extract data from the FDA, NIH, and CDC databases.
AI-Powered Analysis: Integrates Langchain for intelligent data processing and generation of meaningful insights from complex clinical trial information.
User-Friendly Interface: Offers a simple and intuitive interface for querying and viewing data.
Real-Time Updates: Automatically updates the repository with the latest information from the connected databases.
Customizable Searches: Allows users to tailor their searches based on specific criteria such as disease, drug, trial phase, location, and more.
Data Visualization: Presents data in a clear and visually appealing format, making it easier to interpret and analyze.

Technology Stack

Langchain: For generative AI capabilities and natural language processing.
Python: Primary programming language for backend development.
Flask/Django: Web framework for building the application interface.
Beautiful Soup/Scrapy: For web scraping and data extraction.
Pandas/Numpy: For data manipulation and analysis.
JavaScript/HTML/CSS: For front-end development.

Getting Started

Clone the Repository: git clone [repository-link]
Install Dependencies: Run pip install -r requirements.txt
Set Up Environment Variables: Configure necessary API keys and database connections.
Run the Application: Execute python app.py or relevant command to start the server.
Access the Web Interface: Open your browser and go to http://localhost:5000 or the specified port.

Usage

Navigate to the search interface.
Enter your search criteria.
View and analyze the results presented in tables and charts.

Contributing

Contributions to enhance the functionality, improve the UI, or extend the data sources are warmly welcomed. Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests.

License

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

Acknowledgments

Special thanks to the Langchain community for their invaluable tools and support.

SVJayanthi / GenAIClinicalTrials