PhysioXGluNet: a physiology-informed network architecture with attributable branches for explainable blood glucose prediction

For people with type 1 diabetes (T1D), poor management of blood glucose (BG) can lead to hyperglycemia and hypoglycemia, which are the main culprits of health risks. Blood glucose prediction plays a crucial role in BG management. Patients can understand future BG change trends and take actions in advance to maintain euglycemia. Multivariate prediction models based on deep learning have achieved outstanding performance, but insufficient consideration of physiological consistency and physiological explainability has led to a lack of clinical confidence. To address this, we propose a novel physiology-informed network architecture with attributable branches, named PhysioXGluNet, which structurally integrates domain knowledge and individual data. To ensure physiological consistency, we determine the branch composition according to the physiological compartment model and also design the physiology-informed recurrent neural network and physiology-informed composite loss. To enhance physiological explainability, PhysioXGluNet not only outputs future BG levels but also provides the net BG changes caused by each input variable via corresponding attributable branch, allowing a clear traceability of the dominant factors at each time step driving future BG changes. PhysioXGluNet outperforms baseline models in both numerical and clinical metrics when evaluated on the real-world dataset. PhysioXGluNet has the potential to assist clinical decision-making in T1D BG management for this studied cohort.