COMMUTE: Communication-efficient transfer learning for multi-site risk prediction.

Journal: Journal of biomedical informatics
Published Date: Nov 18, 2022

Abstract

OBJECTIVES: We propose a communication-efficient transfer learning approach (COMMUTE) that effectively incorporates multi-site healthcare data for training a risk prediction model in a target population of interest, accounting for challenges including population heterogeneity and data sharing constraints across sites.

Authors

  • Tian Gu
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
  • Phil H Lee
    Department of Psychiatry, Harvard Medical School, Boston, MA, United States; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States.
  • Rui Duan
    Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA.

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