A personalized federated learning-based glucose prediction algorithm for high-risk glycemic excursion regions in type 1 diabetes.

ABSTRACT: Continuous glucose monitoring (CGM) devices allow real-time glucose readings leading to improved glycemic control. However, glucose predictions in the lower (hypoglycemia) and higher (hyperglycemia) extremes, referred as glycemic excursions, remain challenging due to their rarity. Moreover, limited access to sensitive patient data hampers the development of robust machine learning models even with advanced deep learning algorithms available. We propose to simultaneously provide accurate glucose predictions in the excursion regions while addressing data privacy concerns. To tackle excursion prediction, we propose a novel Hypo-Hyper (HH) loss function that penalizes errors based on the underlying glycemic range with a higher penalty at the extremes over the normal glucose range. On the other

SUBMITTER: Darpit D 

PROVIDER: S-EPMC12583674 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature