Generalized multi task learning framework for glucose forecasting and hypoglycemia detection using simulation to reality.

ABSTRACT: Continuous prediction of glucose levels and hypoglycemia events is critical for managing type 1 diabetes mellitus (T1DM) under intensive insulin therapy. Existing models focus on a single task, limiting their practicality and adaptability in automated insulin delivery (AID) systems. To address this, a domain-agnostic continual multi-task learning (DA-CMTL) framework that simultaneously performs glucose level forecasting and hypoglycemia event classification within a unified framework is proposed. Trained on simulated datasets via Sim2Real transfer and adapted using elastic weight consolidation, DA-CMTL supports cross-domain generalization. Evaluation on public datasets (DiaTrend, OhioT1DM, and ShanghaiT1DM) yielded a root mean squared error of 14.01 mg/dL, mean absolute error of 10.03 mg/d

SUBMITTER: Hwang M 

PROVIDER: S-EPMC12531325 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature