用强化学习从大数据生成个性化糖尿病生活方式建议。
Diabetes Lifestyle Medicine Treatment Assistance Using Reinforcement Learning
- 基于NHANES数据，用离线强化学习生成个体化生活处方。
- 模型在3位医生的处方上表现相当，风险预测准确率高。
- 适合医疗资源不足地区或需大规模个性化干预的场景。
2型糖尿病的预防与治疗可通过个性化生活方式干预获益。然而，专业人员短缺和医生经验差异限制了个性化方案的实施。本文提出一种离线上下文老虎机方法，基于119,555名参与者整合的NHANES数据，通过最小化Magni血糖风险-奖励函数，学习个体化生活方式处方。模型编码患者状态并生成干预建议，采用混合动作软演员-批评家（mixed-action SAC）算法训练，任务被建模为单步上下文老虎机。模型在中南大学湘雅医院三位认证医师的生活方式处方上进行验证，结果表明离线混合动作SAC可从横断面NHANES数据中生成具有风险意识的生活方式建议，具备前瞻性临床验证价值。
原文摘要 · Abstract (English)
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.
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