Subject-Conditioned Glucose Forecasting in Type-1 Diabetes

TL;DR - SCGP is a multimodal deep-learning architecture for personalized blood-glucose forecasting in Type 1 Diabetes. Explicitly conditioning predictions on learned subject representations improves forecasting and detection of adverse glycemic events across multiple horizons.

• Separates subject characterization from temporal glucose-dynamics modeling.
• Learns compact subject-specific representations from contextual information.
• Avoids early fusion of heterogeneous inputs to preserve robust temporal modeling.
• Consistently improves performance on two benchmark datasets.