Language-model-based patient embedding using electronic health records facilitates phenotyping, disease forecasting, and progression analysis.

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Bibliographic Details
Title: Language-model-based patient embedding using electronic health records facilitates phenotyping, disease forecasting, and progression analysis.
Authors: Xian S; Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA., Grabowska ME; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN., Kullo IJ; Department of Cardiovascular Medicine and the Gonda Vascular Center, Mayo Clinic Rochester Minnesota., Luo Y; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine., Smoller JW; Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA.; Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA., Wei WQ; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN., Jarvik G; Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA., Mooney S; Center for Information Technology, National Institutes of Health., Crosslin D; Department of Medicine, Division of Biomedical Informatics and Genomics, Tulane University, New Orleans, LA.
Source: Research square [Res Sq] 2024 Sep 23. Date of Electronic Publication: 2024 Sep 23.
Publication Type: Journal Article; Preprint
Journal Info: Country of Publication: United States NLM ID: 101768035 Publication Model: Electronic Cited Medium: Internet ISSN: 2693-5015 (Electronic) Linking ISSN: 26935015 NLM ISO Abbreviation: Res Sq Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2693-5015
DOI:10.21203/rs.3.rs-4708839/v1