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

• URL: http://arxiv.org/abs/2111.07455v1
• Date: Sun, 14 Nov 2021 21:32:42 GMT
• Title: HAD-Net: Hybrid Attention-based Diffusion Network for Glucose Level Forecast
• Authors: Quentin Blampey and Mehdi Rahim
• Abstract summary: HAD-Net is 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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