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Data-driven models for glucose level forecast often do not provide meaningfulinsights despite accurate predictions. Yet, context understanding in medicineis crucial, in particular for diabetes management. In this paper, we introduceHAD-Net: a hybrid model that distills knowledge into a deep neural network fromphysiological models. It models glucose, insulin and carbohydrates diffusionthrough a biologically inspired deep learning architecture tailored with arecurrent attention network constrained by ODE expert models. We apply HAD-Netfor glucose level forecast of patients with type-2 diabetes. It achievescompetitive performances while providing plausible measurements of insulin andcarbohydrates diffusion over time.

HAD-Net: Hybrid Attention-based Diffusion Network for Glucose Level Forecast