Personalized Physiological-Model-Based Neural Network for Glucose Forecasting in Type 1 Diabetes
Accurate, physiologically consistent glucose forecasts could help people with type 1 diabetes respond to impending excursions. We applied our physiological-model-based neural network (PMB--NN) framework to glucose forecasting. A temporal convolutional network (TCN) used 180 min of history, constrained during training by a personalized four-parameter physiological model. The main PMB--NN combined forecast and neural--physiological agreement losses; a three-term variant additionally fitted physiological predictions to measured glucose. Data comprised 6--8 weeks of continuous glucose monitoring, pump, and meal records from 25 AZT1D participants. Person-specific models used chronological 70/1...
| Authors | Yaowen Zhang, Shuhao Que, Ying Wang |
|---|---|
| Primary category | physics.med-ph |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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