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Weakly Semi-supervised phenotyping using Electronic Health records.

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第一作者： Isabelle-Emmanuella,Nogues

第一单位： Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.

作者： Isabelle-Emmanuella,Nogues ^[1] ; Jun,Wen ^[2] ; Yucong,Lin ^[3] ; Molei,Liu ^[1] ; Sara K,Tedeschi ^[4] ; Alon,Geva ^[5] ; Tianxi,Cai ^[6] ; Chuan,Hong ^[7]

作者单位： Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA. ^[1] Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. ^[2] Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Center for Statistical Science, Tsinghua University, Beijing, China. ^[3] Department of Medicine, Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Boston, MA, USA. ^[4] Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Department of Anesthesiology, Critical Care, and Pain Medicine, and Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA; Department of Anesthesia, Harvard Medical School, Boston, MA, USA. ^[5] Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. ^[6] Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. Electronic address: Chuan_Hong@hms.harvard.edu. ^[7]

关键词 Deep learning EHR phenotyping Label efficient Silver-standard labels Weakly supervised

医学主题词算法(Algorithms);电子健康病历(Electronic Health Records);Logistic模型(Logistic Models);表型(Phenotype)

DOI 10.1016/j.jbi.2022.104175

PMID 36064111

发布时间 2023-04-21

基金项目

K23 AR075070/AR/NIAMS NIH HHS/United States L30 AR070514/AR/NIAMS NIH HHS/United States R01 LM013614/LM/NLM NIH HHS/United States R21 CA242940/CA/NCI NIH HHS/United States

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