A platform for phenotyping disease progression and associated longitudinal risk factors in large-scale EHRs, with application to incident diabetes complications in the UK Biobank.

Kim, Do Hyun; Jensen, Aubrey; Jones, Kelly; Raghavan, Sridharan; Phillips, Lawrence S; Hung, Adriana; Sun, Yan V; Li, Gang; Reaven, Peter; Zhou, Hua; Zhou, Jin J

Kim, Do Hyun; Jensen, Aubrey; Jones, Kelly; Raghavan, Sridharan; Phillips, Lawrence S; Hung, Adriana; Sun, Yan V; Li, Gang; Reaven, Peter; Zhou, Hua; Zhou, Jin J.

Affiliation

Kim DH; Department of Biostatistics, University of California, Los Angeles, California, USA.
Jensen A; Department of Biostatistics, University of California, Los Angeles, California, USA.
Jones K; Department of Computer Science, Columbia University, New York, New York, USA.
Raghavan S; Division of Hospital Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA.
Phillips LS; Rocky Mountain Regional VA Medical Center, Aurora, Colorado, USA.
Hung A; Division of Endocrinology, Emory University School of Medicine, Atlanta, Georgia, USA.
Sun YV; Atlanta VA Medical Center, Decatur, Georgia, USA.
Li G; VA Tennessee Valley Healthcare System, Nashville, Tennessee, USA.
Reaven P; Vanderbilt University, Nashville, Tennessee, USA.
Zhou H; Department of Epidemiology, Emory University, Atlanta, Georgia, USA.
Zhou JJ; Department of Biostatistics, University of California, Los Angeles, California, USA.

JAMIA Open ; 6(1): ooad006, 2023 Apr.

Article in En | MEDLINE | ID: mdl-36789288

Key words

diabetes; diabetes complications; disease progression; electronic health records; phenotyping; time-to-event

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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: JAMIA Open Year: 2023 Document type: Article Affiliation country:

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