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InfEHR: Clinical phenotype resolution through deep geometric learning on electronic health records.

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Title: InfEHR: Clinical phenotype resolution through deep geometric learning on electronic health records.
Authors: Kauffman J; The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.; The Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.; The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.; Department of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA., Holmes E; The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.; Department of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.; Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.; Division of Newborn Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA., Vaid A; The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.; The Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.; The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.; Department of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA., Charney AW; The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, New York, USA., Kovatch P; The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.; The Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.; The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.; Department of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA., Lampert J; The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.; Department of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.; Valentin Fuster Heart Hospital, Icahn School of Medicine at Mount Sinai, New York City, NY, USA., Sakhuja A; The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.; The Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.; The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.; Department of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.; Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA., Zitnik M; Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA.; Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA, USA., Glicksberg BS; The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.; The Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.; The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.; Department of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA., Hofer I; The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.; The Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.; The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.; Department of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.; Department of Surgery, Icahn School of Medicine at Mount Sinai, New York City, New York, USA., Nadkarni GN; The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA. girish.nadkarni@mountsinai.org.; The Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA. girish.nadkarni@mountsinai.org.; The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, New York, USA. girish.nadkarni@mountsinai.org.; Department of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA. girish.nadkarni@mountsinai.org.; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York City, New York, USA. girish.nadkarni@mountsinai.org.
Source: Nature communications [Nat Commun] 2025 Sep 26; Vol. 16 (1), pp. 8475. Date of Electronic Publication: 2025 Sep 26.
Publication Type: Journal Article
Journal Info: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE
Database: MEDLINE Ultimate
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ISSN:2041-1723
DOI:10.1038/s41467-025-63366-6