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

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.

Methods

We first train population-specific source models locally within each site. Using data from a given target population, COMMUTE learns a calibration term for each source model, which adjusts for potential data heterogeneity through flexible distance-based regularizations. In a centralized setting where multi-site data can be directly pooled, all data are combined to train the target model after calibration. When individual-level data are not shareable in s

SUBMITTER: Gu T 

PROVIDER: S-EPMC9868117 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature