GUIDE: Reinforcement Learning for Behavioral Action Support in Type 1 Diabetes

arXiv:2604.00385v2 Announce Type: replace Abstract: Type 1 diabetes (T1D) management requires continuous adjustment of insulin and lifestyle behaviors to maintain blood glucose within a safe target range. Although automated insulin delivery (AID) systems have improved glycemic outcomes, many patients still fail to achieve recommended clinical targets. Current reinforcement learning (RL)-based methods focus primarily on insulin-only treatment and do not provide behavioral recommendations for glucose control. To address this gap, we propose GUIDE, an RL-based decision-support framework designed to complement AID technologies by providing structured behavioral recommendations defined by intervention type, magnitude, and timing, including bolus insulin administration and carbohydrate intake events. GUIDE integrates a patient-specific glucose predictor trained on real-world continuous glucose monitoring data and supports offline and online RL algorithms within a unified environment. The algorithms are evaluated across 25 individuals from the AZT1D dataset and 12 individuals from the OhioT1DM dataset. Among the evaluated algorithms, CQL-BC achieved the best performance on both datasets, with mean time-in-range values of 84.18 pm 19.89% on AZT1D and 77.64 pm 8.81% on OhioT1DM. It also maintained time-below-range values of 0.43 pm 1.27% and 2.81 pm 3.56%, respectively. Behavioral analysis yielded mean cosine similarities of 0.767 pm 0.128 on AZT1D and 0.774 pm 0.162 on OhioT1DM, indicating that the learned policy preserves key structural characteristics of patient action patterns. These findings demonstrate the potential of conservative offline RL with a structured behavioral action space to provide personalized and behaviorally plausible decision support for diabetes management.

Source: arXiv cs.LG | 2026-10-06