MedGNN (WWW 2025)

The repo is the official implementation for the paper: "Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification".

Getting Started

Environment Requirements

To get started, ensure you have conda installed on your system and follow these steps to set up the environment:

conda create -n MedGNN python=3.9
conda activate MedGNN
pip install -r requirements.txt

Datasets

APAVA dataset: https://drive.google.com/file/d/1FKvUnB8qEcHng7J9CfHaU7gqRLGeS7Ea/view?usp=drive_link.

ADFD dataset: https://drive.google.com/file/d/1QcX_M58IQUBn3lDBlVVL0SDN7_QI1vWe/view.

PTB dataset: https://drive.google.com/file/d/14fBIXc2gSHm00wLaejNIsPgitc-wZdXu/view.

PTB-XL dataset: https://drive.google.com/file/d/1whskRvTZUNb1Qph2SeXEdpcU2rQY0T1E/view.

Training Example

You can reproduce the experiment results as the following examples:

bash ./scripts/ADFD_Sample.sh
bash ./scripts/APAVA_Subject.sh

Acknowledgement

We appreciate the following GitHub repositories for providing valuable code bases and datasets:

Time-Series-Library: https://github.com/thuml/Time-Series-Library

Medformer: https://github.com/DL4mHealth/Medformer

iTransformer: https://github.com/thuml/iTransformer

PatchTST: https://github.com/yuqinie98/PatchTST

FEDformer: https://github.com/MAZiqing/FEDformer

Crossformer: https://github.com/Thinklab-SJTU/Crossformer

FourierGNN: https://github.com/aikunyi/FourierGNN

CrossGNN: https://github.com/hqh0728/CrossGNN

TodyNet: https://github.com/liuxz1011/TodyNet

SimTSC: https://github.com/daochenzha/SimTSC