Interpretable time-aware and co-occurrence-aware network for medical prediction
Abstract Background Disease prediction based on electronic health records (EHRs) is essential for personalized healthcare. But it’s hard due to the special data structure and the interpretability requirement of methods. The structure of EHR is hierarchical: each patient has a sequence of admissions,...
Saved in:
Main Authors: | Chenxi Sun, Hongna Dui, Hongyan Li |
---|---|
Format: | article |
Language: | EN |
Published: |
BMC
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/05dcb3c64a9b4748815c61cc95dcf91c |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection
by: S. Sankar Ganesh, et al.
Published: (2021) -
iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients
by: Jun Wang, et al.
Published: (2021) -
Medical educators’ reflection on how technology sustained medical education in the most critical times and the lessons learnt: Insights from an African medical school
by: Joshua Owolabi, et al.
Published: (2021) -
Implementation challenges and perception of care providers on Electronic Medical Records at St. Paul’s and Ayder Hospitals, Ethiopia
by: Alemayehu Bisrat, et al.
Published: (2021) -
Interpretable survival prediction for colorectal cancer using deep learning
by: Ellery Wulczyn, et al.
Published: (2021)