Integrating Neural Differential Forecasting with Safe Reinforcement Learning for Blood Glucose Regulation

• URL: http://arxiv.org/abs/2511.12417v1
• Date: Sun, 16 Nov 2025 02:11:33 GMT
• Title: Integrating Neural Differential Forecasting with Safe Reinforcement Learning for Blood Glucose Regulation
• Authors: Yushen Liu, Yanfu Zhang, Xugui Zhou,
• Abstract summary: TSODE is a safety-aware controller that integrates Thompson RL Sampling with a Neural Ordinary Differential Equation forecaster.<n>In the FDA-approved UVa/Padova simulator (adult cohort), TSODE achieved 87.9% time-in-range with less than 10% time below 70 mg/dL.
• Score: 51.12307713554633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated insulin delivery for Type 1 Diabetes must balance glucose control and safety under uncertain meals and physiological variability. While reinforcement learning (RL) enables adaptive personalization, existing approaches struggle to simultaneously guarantee safety, leaving a gap in achieving both personalized and risk-aware glucose control, such as overdosing before meals or stacking corrections. To bridge this gap, we propose TSODE, a safety-aware controller that integrates Thompson Sampling RL with a Neural Ordinary Differential Equation (NeuralODE) forecaster to address this challenge. Specifically, the NeuralODE predicts short-term glucose trajectories conditioned on proposed insulin doses, while a conformal calibration layer quantifies predictive uncertainty to reject or scale risky actions. In the FDA-approved UVa/Padova simulator (adult cohort), TSODE achieved 87.9% time-in-range with less than 10% time below 70 mg/dL, outperforming relevant baselines. These results demonstrate that integrating adaptive RL with calibrated NeuralODE forecasting enables interpretable, safe, and robust glucose regulation.

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