DyGraphTrans: A temporal graph representation learning framework for modeling disease progression from Electronic Health Records.

ABSTRACT:

Motivation

Electronic Health Records (EHRs) contain vast amounts of longitudinal patient medical history data, making them highly informative for early disease prediction. Numerous computational methods have been developed to leverage EHR data; however, many process multiple patient records simultaneously, resulting in high memory consumption and computational cost. Moreover, these models also often lack interpretability, limiting insight into the factors driving their predictions. Efficiently handling large-scale EHR data while maintaining predictive accuracy and interpretability therefore remains a critical challenge. To address this gap, we propose DyGraphTrans, a dynamic graph representation learning framework that represents patient EHR data as a sequence of temporal graphs. I

SUBMITTER: Rahman MT 

PROVIDER: S-EPMC12893049 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature