Data-driven models for glucose level forecast often do not provide meaningful insights despite accurate predictions. Yet, context understanding in medicine is crucial, in particular for diabetes management. In this paper, we introduce HAD-Net: a hybrid model that distills knowledge into a deep neural network from physiological models. It models glucose, insulin and carbohydrates diffusion through a biologically inspired deep learning architecture tailored with a recurrent attention network constrained by ODE expert models. We apply HAD-Net for glucose level forecast of patients with type-2 diabetes. It achieves competitive performances while providing plausible measurements of insulin and carbohydrates diffusion over time.

翻译：尽管有准确的预测,但由数据驱动的葡萄糖水平预测模型往往无法提供有意义的真知灼见。然而,医学背景理解至关重要,特别是糖尿病管理。在本文中,我们引入了HAD-Net:一种混合模型,将知识从生理模型中蒸馏到深层神经网络中。它通过生物启发的深层学习结构,根据ODE专家模型所限制的经常性关注网络,制作了葡萄糖、胰岛素和碳水化合物扩散模型。我们用HAD-Net对二型糖尿病患者进行葡萄糖水平预报。它实现了竞争性性能,同时提供了对胰岛素和碳水化合物长期扩散的可信测量。