A multimodal deep learning architecture for predicting interstitial glucose for effective type 2 diabetes management.

ABSTRACT: The accurate prediction of blood glucose is critical for the effective management of diabetes. Modern continuous glucose monitoring (CGM) technology enables real-time acquisition of interstitial glucose concentrations, which can be calibrated against blood glucose measurements. However, a key challenge in the effective management of type 2 diabetes lies in forecasting critical events driven by glucose variability. While recent advances in deep learning enable modeling of temporal patterns in glucose fluctuations, most of the existing methods rely on unimodal inputs and fail to account for individual physiological differences that influence interstitial glucose dynamics. These limitations highlight the need for multimodal approaches that integrate additional personalized physiological infor

SUBMITTER: Haleem MS 

PROVIDER: S-EPMC12307872 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature