Hypergraph Transformers for EHR-based Clinical Predictions.

• Type of Material: Article
• Authors: Ran Xu, Emory University; Mohammed Ali, Emory University; Joyce Ho, Emory University; Carl Yang, Emory University
• Language: English
• Date: 2023
• Publisher: AMIA
• Publication Version: Final Published Version
• Copyright Statement: ©2023 AMIA - All rights reserved.
• Title of Journal or Parent Work: AMIA Jt Summits Transl Sci Proc
• Volume: 2023
• Start Page: 582
• End Page: 591
• Keywords: Electronic health records (EHR); data; Hypergraph Transformers
• Research Categories: Health Sciences, Public Health; Computer Science

Abstract

Electronic health records (EHR) data contain rich information about patients' health conditions including diagnosis, procedures, medications and etc., which have been widely used to facilitate digital medicine. Despite its importance, it is often non-trivial to learn useful representations for patients' visits that support downstream clinical predictions, as each visit contains massive and diverse medical codes. As a result, the complex interactions among medical codes are often not captured, which leads to substandard predictions. To better model these complex relations, we leverage hypergraphs, which go beyond pairwise relations to jointly learn the representations for visits and medical codes. We also propose to use the self-attention mechanism to automatically identify the most relevant medical codes for each visit based on the downstream clinical predictions with better generalization power. Experiments on two EHR datasets show that our proposed method not only yields superior performance, but also provides reasonable insights towards the target tasks.