Automated phenotyping

Project description

Repository for the manuscript entitled "Comparative neural word embeddings approaches for medical concept representation and patient trajectory prediction".

• This project aims to compare NLP models (word2vec, fastTex and GloVe), based on the quality of their representation of medical concepts.
• We use MIMIC-IV to train the models, from which we extract patient trajectories as sequences of (amongst others) ICD10 and ATC codes.
• We train the models with model-specific NLP tasks that use patient trajectory sequences as input.
• We evaluate the models by producing medical concept embeddings, clustering them, comparing them to existing biomedical terminologies, and using them for medical outcome and patient trajectory prediction tasks.

Installation requirements

1. Clone the repository:

   git clone git@github.com:ds4dh/medical_concept_representation.git  # or https://github.com/ds4dh/medical_concept_representation.git
   cd medical_concept_representation

2. Install the required dependencies:

   ./create_env.sh
   conda activate medical_representation

   If you have version issues, you can build an environment with the packages listed in environment.yml

3. The project uses WandbLogger for experiment tracking. Ensure you have a Weights & Biases account set up for logging.

Usage

You need to download the data yourself! Instructions for downloading and pre-processing the data are here: https://github.com/ds4dh/medical_concept_representation/tree/main/data

Once the pre-processed data is ready, train the models with:

python run_all_models.py  # long step, best in screen https://linuxize.com/post/how-to-use-linux-screen/

Once the models are trained, test the trained models with:

python run_all_models.py -t  # long step, best in screen https://linuxize.com/post/how-to-use-linux-screen/

Result figures 4, 5, and 7 will be available at your wandb log page.

For the other result figures, run:

python figures/figure_6.py
python figures/figure_8.py
python figures/figure_8_bis.py   # supplementary figures
python figures/figure_9.py