Recent Advances in Modeling and Prediction of Blood Glucose in Type 1 Diabetes

Abstract

Accurate prediction and control of blood glucose levels are essential for the management of type 1 diabetes, where patients rely on exogenous insulin and are vulnerable to both hypoglycemia and hyperglycemia. The widespread adoption of continuous glucose monitoring systems, insulin pumps, and wearable devices has generated large volumes of physiological and behavioral data, creating new opportunities for computational modeling and intelligent decision support. This review surveys recent advances in glucose prediction and control models, with a primary focus on type 1 diabetes. We examine three major classes of approaches: mechanistic models based on physiological principles, data-driven machine learning methods, and hybrid or biology-informed frameworks that integrate mechanistic knowledge with learning-based techniques. We also discuss the growing role of multimodal data, deep learning architectures, and reinforcement learning for automated insulin dosing and adaptive control in artificial pancreas systems. Despite significant progress, important challenges remain, including handling noisy and heterogeneous data, improving predictive reliability and uncertainty quantification, and enabling real-time deployment on resource-constrained medical devices. Emerging strategies such as edge computing, efficient model design, and hardware–algorithm co-optimization may help bridge this gap. Continued progress will require interdisciplinary collaboration, standardized evaluation on public datasets, and rigorous clinical validation to translate emerging modeling approaches into practical tools that improve patient outcomes.