Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction.

ABSTRACT:

Background

Machine learning and deep learning are powerful tools for analyzing electronic health records (EHRs) in healthcare research. Although family health history has been recognized as a major predictor for a wide spectrum of diseases, research has so far adopted a limited view of family relations, essentially treating patients as independent samples in the analysis.

Methods

To address this gap, we present ALIGATEHR, which models inferred family relations in a graph attention network augmented with an attention-based medical ontology representation, thus accounting for the complex influence of genetics, shared environmental exposures, and disease dependencies.

Results

Taking disease risk prediction as a use case, we demonstrate that explicitly modeling family rel

SUBMITTER: Huang X 

PROVIDER: S-EPMC11833479 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature