{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T06:27:50Z","timestamp":1769840870622,"version":"3.49.0"},"reference-count":56,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T00:00:00Z","timestamp":1735171200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Yale-Boehringer Ingelheim biomedical data science","award":["AWD0006462"],"award-info":[{"award-number":["AWD0006462"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Taking disease risk prediction as a use case, we demonstrate that explicitly modeling family relations significantly improves predictions across the disease spectrum. We then show how ALIGATEHR\u2019s attention mechanism, which links patients\u2019 disease risk to their relatives\u2019 clinical profiles, successfully captures genetic aspects of diseases using longitudinal EHR diagnosis data. Finally, we use ALIGATEHR to successfully distinguish the 2 main inflammatory bowel disease subtypes with highly shared risk factors and symptoms (Crohn\u2019s disease and ulcerative colitis).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Overall, our results highlight that family relations should not be overlooked in EHR research and illustrate ALIGATEHR\u2019s great potential for enhancing patient representation learning for predictive and interpretable modeling of EHRs.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocae297","type":"journal-article","created":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T13:40:31Z","timestamp":1735220431000},"page":"435-446","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction"],"prefix":"10.1093","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3730-5014","authenticated-orcid":false,"given":"Xiayuan","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Yale University School of Public Health , New Haven, CT 06510,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jatin","family":"Arora","sequence":"additional","affiliation":[{"name":"Human Genetics, Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharma GmbH & Co. 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