Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network.

Autor: Naderalvojoud B; Department of Medicine, Stanford University, Stanford, CA 94305, United States., Curtin CM; Department of Surgery, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, United States., Yanover C; KI Research Institute, Kfar Malal, 4592000, Israel., El-Hay T; KI Research Institute, Kfar Malal, 4592000, Israel., Choi B; Department of Biomedical Informatics, Ajou University Graduate School of Medicine, Suwon, 16499, Korea., Park RW; Department of Biomedical Informatics, Ajou University Graduate School of Medicine, Suwon, 16499, Korea., Tabuenca JG; Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, 00014, Finland., Reeve MP; Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, 00014, Finland., Falconer T; Department of Biomedical Informatics, Columbia University, New York, NY 10032, United States., Humphreys K; Department of Psychiatry and the Behavioral Sciences, Stanford University, Stanford, CA 94305, United States.; Center for Innovation to Implementation, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, United States., Asch SM; Department of Medicine, Stanford University, Stanford, CA 94305, United States.; Center for Innovation to Implementation, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, United States., Hernandez-Boussard T; Department of Medicine, Stanford University, Stanford, CA 94305, United States.

Publikováno v: Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2024 Apr 19; Vol. 31 (5), pp. 1051-1061.