ku-milab / miam

Pytorch implementation of "Multi-view Integration Learning for Irregularly-sampled Clinical Time Series" (Under review, JBHI)

MIAM

This repository provides the official PyTorch implementation of the following paper:

Multi-view Integration Learning for Irregularly-sampled Clinical Time Series (MIAM)
Yurim Lee¹, Eunji Jun¹, Heung-Il Suk¹ (¹Korea University)
[arXiv version]

Under review, Journal of Biomedical and Health Informatics

Files description

MIAM

• main.py
• models.py: contains the MIAM
• helpers.py: helper functions for running models

Extended

Includes the extended version for Journal (Under Review)

• lrp.py: Layer-wise Relevance Propagation code for analysis

Acknowledgements

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Program(Korea University))