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

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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
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  Data: Semi-supervised Double Deep Learning Temporal Risk Prediction (SeDDLeR) with Electronic Health Records.
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  Data: <searchLink fieldCode="AU" term="%22Nogues+IE%22">Nogues IE</searchLink>; Department of Biostatistics, Harvard T.H. Chan School of Public Health, United States of America.<br /><searchLink fieldCode="AU" term="%22Wen+J%22">Wen J</searchLink>; Department of Biomedical Informatics, Harvard Medical School, United States of America.<br /><searchLink fieldCode="AU" term="%22Zhao+Y%22">Zhao Y</searchLink>; Harvard College, Harvard University, United States of America.<br /><searchLink fieldCode="AU" term="%22Bonzel+CL%22">Bonzel CL</searchLink>; Department of Biomedical Informatics, Harvard Medical School, United States of America.<br /><searchLink fieldCode="AU" term="%22Castro+VM%22">Castro VM</searchLink>; Research Information Science and Computing, Mass General Brigham Healthcare, United States of America.<br /><searchLink fieldCode="AU" term="%22Lin+Y%22">Lin Y</searchLink>; Institute of Engineering Medicine, Beijing Institute of Technology, China.<br /><searchLink fieldCode="AU" term="%22Xu+S%22">Xu S</searchLink>; Department of Statistics, University of Connecticut, United States of America.<br /><searchLink fieldCode="AU" term="%22Hou+J%22">Hou J</searchLink>; Division of Biostatistics, School of Public Health, University of Minnesota, United States of America. Electronic address: hou00123@umn.edu.<br /><searchLink fieldCode="AU" term="%22Cai+T%22">Cai T</searchLink>; 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.
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  Data: <searchLink fieldCode="JN" term="%22100970413%22">Journal of biomedical informatics</searchLink> [J Biomed Inform] 2024 Sep; Vol. 157, pp. 104685. <i>Date of Electronic Publication: </i>2024 Jul 14.
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        Value: 10.1016/j.jbi.2024.104685
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      – TitleFull: Semi-supervised Double Deep Learning Temporal Risk Prediction (SeDDLeR) with Electronic Health Records.
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            – D: 01
              M: 09
              Text: 2024 Sep
              Type: published
              Y: 2024
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              Value: 157
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