Diabetes Lifestyle Medicine Treatment Assistance Using Reinforcement Learning
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Abstract (arXiv)
Type 2 diabetes prevention and treatment can benefit from personalized lifestyle prescriptions. However, the delivery of personalized lifestyle medicine prescriptions is limited by the shortage of trained professionals and the variability in physicians' expertise. We propose an offline contextual bandit approach that learns individualized lifestyle prescriptions from the aggregated NHANES profiles of 119,555 participants by minimizing the Magni glucose risk-reward function. The model encodes patient status and generates lifestyle medicine prescriptions, which are trained using a mixed-action Soft Actor-Critic algorithm. The task is treated as a single-step contextual bandit. The model is validated against lifestyle medicine prescriptions issued by three certified physicians from Xiangya Hospital. These results demonstrate that offline mixed-action SAC can generate risk-aware lifestyle medicine prescriptions from cross-sectional NHANES data, warranting prospective clinical validation.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 26 references that are in the map. First posted on arXiv 19 Oct 2025.
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5 of 26 references.
References · 5
- 2016A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patientscited by 38
- 2020Deep Reinforcement Learning for Closed-Loop Blood Glucose Controlcited by 26
- 2022Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetescited by 22
- 2020Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validationcited by 18
- 2017An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventionscited by 17
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