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MediQ: Question-Asking LLMs for Adaptive and Reliable Clinical Reasoning

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Overview

This benchmark system simulates an interactive conversation between a patient and an expert. The system evaluates how well participants' expert modules can handle realistic patient queries by either asking relevant questions or making final decisions based on the conversation history.

Requirements

Python 3.8 or higher
Necessary Python libraries: json, os, logging, importlib

Installation

Clone this repository to your local machine using the following command:

git clone https://github.com/stellali7/MediQ.git

Navigate into the project directory:

cd MediQ

Install the required Python libraries (if not already installed):

pip install -r requirements.txt

Project Structure

benchmark.py: Main script to run the benchmark.
patient.py: Defines the Patient class that simulates patient behavior.
expert.py: Contains the Expert class which participants will extend to implement their response strategies.
args.py: Handles command-line arguments for the benchmark system.

Configuration

Before running the benchmark, configure the necessary parameters in args.py:

--expert_module: The module name where the participant's expert class is implemented.
--expert_class: The name of the expert class to use for the benchmark.
--data_dir: Directory containing the development data files.
--dev_filename: Filename for development data.
--log_filename: Filename for logging general benchmark information.
--history_log_filename: Filename for logging detailed interaction history.
--message_log_filename: Filename for logging messages.
--output_filepath: Path where the output JSONL files will be saved.

Running the Benchmark

To test run the benchmark, use the following command (note: the Patient system is provided as described in the paper, the Expert system is a skeleton code. For a fast test run, use --patient_variant random to not call use any actual model or API):

python mediQ_benchmark.py  --expert_module expert --expert_class Expert --patient_variant random \
                        --data_dir ../data/MedQA --dev_filename all_dev_good.jsonl \
                        --output_filename out.jsonl --max_questions 10

Ensure to replace the placeholder values with actual parameters relevant to your setup.

Try out your own Expert system

Participants are expected to create their own Expert class within a module specified by --expert_module. The class should correctly implement the respond method to interact with the Patient instances based on their states. The response should either be a continuation question or a final decision. Your implementation will be tested against a variety of patient scenarios provided in the development dataset.

How to Cite

@misc{li2024mediq,
      title={MEDIQ: Question-Asking LLMs for Adaptive and Reliable Clinical Reasoning}, 
      author={Shuyue Stella Li and Vidhisha Balachandran and Shangbin Feng and Jonathan Ilgen and Emma Pierson and Pang Wei Koh and Yulia Tsvetkov},
      year={2024},
      eprint={2406.00922},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Shield:

This work is licensed under a Creative Commons Attribution 4.0 International License.