ICD Coding using ChatGPT

This repository contains the code to predict ICD-10 codes from clinical notes using ChatGPT

Table of Contents

• 🛠️ Setup
  • Cloning the codebase
  • Python packages
  • Environment variables
• 💾 Dataset
• 🤖 Inference
• 🔬 Evaluation

🛠️ Setup

Cloning the codebase

git clone --recurse-submodules https://github.com/aryopg/chatgpt_icd_coding.git

Python packages

This codebase requires multiple dependencies.

Dependencies

- pip
- numpy
- pydantic
- python-dotenv
- black
- isort
- tqdm
- pandas
- matplotlib
- scikit-learn

We opted to use conda as our package manager. The following will install the necessary dependencies:

conda env create -f environment.yaml
conda activate chatgpt_icd_coding

Environment variables

There are multiple environment variables required to run the training:

• AZURE_OPENAI_KEY: The key to access the OpenAI deployment on Azure.
• AZURE_OPENAI_ENDPOINT: The endpoint URL to access the exact OpenAI deployment on Azure.

We use the python-dotenv package to load these environment variables. To set them:

mkdir env
nano env/.env

Write down all of the mentioned environment variables with the appropriate values inside that file. Certainly, you don't have to use nano, as long as the file name (env/.env) remains the same.

💾 Dataset

The datasets are generated using: https://github.com/joakimedin/medical-coding-reproducibility We only used the test split for the inference.

🤖 Inference

To run the prediction, we need a config file that contains the hyperparameters of ChatGPT. See the example below for a "deterministic" prediction run (temperature == 0, top_p == 0):

python scripts/inference.py --config_filepath configs/deterministic_chatgpt_mimic_iv_coding_system_user.yaml

🔬 Evaluation

After the inference, there will be an output folder containing all the predictions made by ChatGPT (e.g. outputs/2023_08_25__09_19_37/predictions). We need the path to that folder as well as the path to the ground-truth test split (e.g. data/disch_raw_test_split.csv) to provide the true labels.

python scripts/evaluate.py --predictions_dir <PREDICTIONS_DIR> --groundtruth_path <GROUNDTRUTH_PATH>