Type 1 diabetes mellitus requires exogenous insulin and accurate glucose forecasting is critical for closed-loop artificial pancreas systems. While continuous glucose monitoring provides real-time data, purely data-driven recurrent neural networks may produce physiologically implausible predictions, and purely mechanistic models cannot fully capture individual variability in insulin sensitivity, meal absorption, or exercise response. This framework proposes a physics-guided recurrent neural network that integrates insulin delivery records, carbohydrate intake, and physical activity data. It combines a mechanistic glucose–insulin compartmental model with a residual LSTM network that learns patient-specific deviations, supported by a physics-based loss function enforcing physiological constraints such as non-negativity and realistic glucose dynamics. By merging physiological modeling with deep learning, the system preserves biological plausibility while adapting to individual patient patterns. Incorporating multimodal wearable and device data enables more accurate, longer-horizon glucose predictions, supporting safer and more proactive insulin dosing in closed-loop diabetes management.
Clinical Intelligence Research Press