zhangxiaowbl / GNN_for_EHR

Code for "Graph Neural Network on Electronic Health Records for Predicting Alzheimer’s Disease"

Graph Neural Network on Electronic Health Records for Predicting Alzheimer’s Disease

This repository contains the code for the paper Graph Neural Network on Electronic Health Records for Predicting Alzheimer’s Disease.

Model Training

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

Required packages can be installed on python3.6 environment via command:

pip3 install -r requirements.txt

Nvidia GPU with Cuda 10.0 are required for training models.

Data

A synethic data with same format in data folder:

• preprocess_x.pkl: 1-d EHR data (num_of_patients * num_of_EHRs);
• y_bin.pkl: AD outcomes in 12-24 months;
• frts_selections.pkl: indices of features;
• train_idx.pkl, val_idx.pkl, test_idx.pkl: indices of samples that belongs to train, validation or test sets;
• neg_young.pkl: indices of young negative samples in training set to be downsampled;
• synethic_data_generator: the detail format and method of generating synethic data.

Train

GNN for EHR on predicting disease outcomes can be train by running command:

python3 train.py --input 512 --output 512 --heads 4 --batch 64 --dropout 0.4 --alpha 0.15 --lr 0.0001