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Implementation of BernGraph: Probabilistic Graph Neural Networks for EHR-based Medication Recommendations

Package Dependency

  • Please check the requirements.txt
  • You can create the conda environment and use the requirements.txt file:
pip install -r requirements.txt

Folder Specification

MIMIC-III

  • data.csv: preprocessed raw data
  • label.csv: Ground truth of recommendation
  • run.py: train BernGraph
  • egsage.py: GNN model
  • eval.ipynb: Evaluation file.

AMR-URI

  • data.csv: preprocessed raw data
  • label.csv: Ground truth of recommendation
  • run.py: train BernGraph
  • egsage.py: GNN model
  • eval.ipynb: Evaluation file.

Run the code

First, please unzip the data files inside each folder.

  • Training: Run the "run.py" file in each folder.
  • Model parameter: The best parameter will be saved in the folder "/models".
  • Evaluation: Run the "eval.ipynb" file in each folder.

Train/test baselines

  • COGNet: The code can be found here.
  • Other train/test baseline codes can be found here.

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