longaqiqi/ehragent

[preprint'24] EHRAgent: Code Empowers Large Language Models for Complex Tabular Reasoning on Electronic Health Records

⚕️EHRAgent🤖

The official repository for the code of the paper "EHRAgent: Code Empowers Large Language Models for Complex Tabular Reasoning on Electronic Health Records". EHRAgent is an LLM agent empowered with a code interface, to autonomously generate and execute code for complex clinical tasks within electronic health records (EHRs). The project page is available at this link.

Features

• EHRAgent is an LLM agent augmented with tools and medical knowledge, to solve complex tabular reasoning derived from EHRs;
• Planning with a code interface, EHRAgent enables the LLM agent to formulate a clinical problem-solving process as an executable code plan of action sequences, along with a code executor;
• We introduce interactive coding between the LLM agent and code executor, iteratively refining plan generation and optimizing code execution by examining environment feedback in depth.

Data Preparation

We use the EHRSQL benchmark for evaluation. The original dataset is for text-to-SQL tasks, and we have made adaptations to our evaluation. We release our clean and pre-processed version of EHRSQL-EHRAgent data. Please download the data and record the path of the data.

Credentials Preparation

Our experiments are based on OpenAI API services. Please record your API keys and other credentials in the ./ehragent/config.py.

Setup

See requirements.txt. Packages with versions specified in requirements.txt are used to test the code. Other versions that are not fully tested may also work. We also kindly suggest the users to run this code with Python version: python>=3.9. Install required libraries with the following command:

pip3 install -r requirements.txt

Instructions

The outputting results will be saved under the directory ./logs/. Use the following command to run our code:

python main.py --llm YOUR_LLM_NAME --dataset mimic_iii --data_path YOUR_DATA_PATH --logs_path YOUR_LOGS_PATH --num_questions -1 --seed 0

We also support debugging mode to focus on a single question:

python main.py --llm YOUR_LLM_NAME --dataset mimic_iii --data_path YOUR_DATA_PATH --logs_path YOUR_LOGS_PATH --debug --debug_id QUESTION_ID_TO_DEBUG

For eICU dataset, just change the option of dataset to --dataset eicu.

Citation

If you find this repository useful, please consider citing:

@misc{shi2024ehragent,
      title={EHRAgent: Code Empowers Large Language Models for Complex Tabular Reasoning on Electronic Health Records}, 
      author={Wenqi Shi and Ran Xu and Yuchen Zhuang and Yue Yu and Jieyu Zhang and Hang Wu and Yuanda Zhu and Joyce Ho and Carl Yang and May D. Wang},
      year={2024},
      eprint={2401.07128},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}