PhyNet: A Physiology-Constrained Monotonic Neural Network for Safe and Explainable Blood Glucose Forecasting

Deep learning (DL) has become state-of-the-art for blood glucose (BG) forecasting in type 1 diabetes (T1D). However, its black-box nature raises safety and reliability concerns regarding its use for therapeutic decision-support. This study aims to: (1) highlight potential risks associated with standard DL-based BG forecasting, and (2) address them with PhyNet, a physiology-constrained monotonic neural network.

Two large-scale datasets (T1DEXI and MetaboNet, 848 subjects in total) were used to develop PhyNet-which enforces physiological consistency through a multi-branch structure and weight constraints-and compare it against six DL baselines (convolutional, recurrent, and transformer-based architectures). Models predicted BG levels up to 90-minute ahead using continuous glucose monitoring (CGM) data, carbohydrate intake, and insulin dosing, and were assessed for: (i) predictive accuracy with standard metrics, and (ii) adherence to physiological principles (i.e., carbohydrates increase BG, insulin lowers it) using explainable AI. Specifically, we evaluated model-predicted responses to varying carbohydrate and insulin intakes, and generated counterfactual explanations to identify model-recommended actions for avoiding adverse events.

At a 30-minute horizon, predictive accuracy was similar across models (RMSE: 19.39-21.00 mg/dL; Time Gain: 10.45-13.35 min). Despite this, only PhyNet consistently captured the physiological effects of carbohydrates and insulin, yielding 0% unsafe recommendations versus up to 64.3% for baselines.

Standard DL models can achieve state-of-the-art performance while failing to respect physiology, posing clinical risk. PhyNet preserves accuracy while enhancing physiological fidelity, supporting safer integration into T1D technologies.

Evaluating physiological consistency alongside predictive accuracy is essential for responsible clinical translation of DL-based BG forecasting.