Continual learning across population cohorts with distribution shift: insights from multi-cohort metabolic syndrome identification.

ABSTRACT:

Objective

This study aims to tackle the critical challenge of adapting deep learning (DL) models for deployment in real-world healthcare settings, specifically focusing on catastrophic forgetting due to distribution shifts between hospital and non-hospital environments. Metabolic syndrome (MetS) is susceptible to misdiagnosis by DL models due to distribution shifts. This work demonstrates the potential of continual learning (CL) to enhance model performance in MetS identification across diverse settings.

Materials and methods

We utilized 3 healthcare datasets from 2 distinct settings: Medical Information Mart for Intensive Care (MIMIC; from a hospital setting), National Health and Nutrition Examination Survey (NHANES), and a proprietary dataset (both from non-hospital settin

SUBMITTER: Liu C 

PROVIDER: S-EPMC12277696 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature