Semi-supervised Double Deep Learning Temporal Risk Prediction (SeDDLeR) with Electronic Health Records.

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Bibliographic Details
Title: Semi-supervised Double Deep Learning Temporal Risk Prediction (SeDDLeR) with Electronic Health Records.
Authors: Nogues IE; Department of Biostatistics, Harvard T.H. Chan School of Public Health, United States of America., Wen J; Department of Biomedical Informatics, Harvard Medical School, United States of America., Zhao Y; Harvard College, Harvard University, United States of America., Bonzel CL; Department of Biomedical Informatics, Harvard Medical School, United States of America., Castro VM; Research Information Science and Computing, Mass General Brigham Healthcare, United States of America., Lin Y; Institute of Engineering Medicine, Beijing Institute of Technology, China., Xu S; Department of Statistics, University of Connecticut, United States of America., Hou J; Division of Biostatistics, School of Public Health, University of Minnesota, United States of America. Electronic address: hou00123@umn.edu., Cai T; Department of Biostatistics, Harvard T.H. Chan School of Public Health, United States of America; Department of Biomedical Informatics, Harvard Medical School, United States of America.
Source: Journal of biomedical informatics [J Biomed Inform] 2024 Sep; Vol. 157, pp. 104685. Date of Electronic Publication: 2024 Jul 14.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Country of Publication: United States NLM ID: 100970413 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-0480 (Electronic) Linking ISSN: 15320464 NLM ISO Abbreviation: J Biomed Inform Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1532-0480
DOI:10.1016/j.jbi.2024.104685