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ClinicalAgent

Clinical Agent is an advanced framework leveraging Large Language Models (LLMs) to enhance the efficiency and effectiveness of clinical trials.

Congratulations! This paper has been accepted at the 2024 ACM-BCB Conference!

Overview

Directory Structure

ClinicalAgent/
├── algo/
│   ├── agents/
│   │   ├── tools/
│   │   │   ├── drugbank/
│   │   │   ├── enrollment/
│   │   │   ├── hetionet/
│   │   │   ├── risk_model/
│   ├── main.ipynb
├── web/

• /algo: Contains the core codebase for ClinicalAgent.
• /web: A preliminary web tool for ClinicalAgent based on LLM.

Setup Instructions

Setting the OpenAI API Key

Before starting, set your OpenAI API key by adding the following lines to your ~/.bashrc file:

export OPENAI_API_KEY="sk-xxxxxxxxx"
export NEXT_PUBLIC_OPENAI_API_KEY="sk-xxxxxxxxx"

Dependencies

Ensure you have Python 3.8.19 installed. The algo directory requires the following dependencies:

• pytorch==1.12.1
• torchvision==0.13.1
• torchaudio==0.12.1
• cudatoolkit==11.3
• transformers==4.39.3
• tokenizers==0.15.1
• openai==1.28.0

To set up the environment, run:

python3.8 -m venv venv
source venv/bin/activate
pip install torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit==11.3 transformers==4.39.3 tokenizers==0.15.1 openai==1.28.0

Agents and Tools

Before running ClinicalAgent, follow the README instructions in the drugbank, enrollment, hetionet, and risk_model directories to generate the necessary data for the tools:

• drugbank
• enrollment
• hetionet
• risk_model

Running ClinicalAgent

The primary entry point for running ClinicalAgent is the main.ipynb notebook. This notebook utilizes various agents located in the algo/agents directory.

In main.ipynb, you can switch between using agents and tools or just plain GPT for answering questions by calling solve_problem() or solve_problem_standard(), as shown below:

subproblem_solve, final_result_str = solve_problem(user_problem)

subproblem_solve, final_result_str = solve_problem_standard(user_problem)

Citation

If you use ClinicalAgent in your research, please cite the following paper:

@article{yue2024ct,
  title={CT-Agent: Clinical Trial Multi-Agent with Large Language Model-based Reasoning},
  author={Yue, Ling and Fu, Tianfan},
  journal={arXiv preprint arXiv:2404.14777},
  year={2024}
}

Feel free to reach out if you have any questions or need further assistance.