结合临床知识构建可解释的治疗策略，提升远程监测系统对糖尿病患者的管理效果。
Learning Explainable Treatment Policies with Clinician-Informed Representations: A Practical Approach
- 用医生经验设计状态与动作表示，增强算法可解释性。
- 基于临床知识的策略在血糖控制上显著优于黑箱模型。
- 适合医疗AI研究者与临床医生协作开发真实场景下的健康干预方案。
数字健康干预（DHIs）和远程患者监测（RPM）在改善慢性病管理方面展现出巨大潜力，但现有系统受限于疗效不足、工作负荷大以及纯黑箱算法缺乏可解释性。本文提出一种学习可解释治疗策略的实用流程，应用于使用RPM的青少年1型糖尿病血糖控制场景。核心贡献在于揭示临床领域知识在构建状态与动作表征中的关键作用。结果显示，基于医生认知构建的表示所学策略，在有效性与效率上均显著优于基于黑箱表示的策略。本研究强调了机器学习研究人员与临床医生合作开发真实世界有效DHIs的重要性。
原文摘要 · Abstract (English)
Digital health interventions (DHIs) and remote patient monitoring (RPM) have shown great potential in improving chronic disease management through personalized care. However, barriers like limited efficacy and workload concerns hinder adoption of existing DHIs; while limited sample sizes and lack of interpretability limit the effectiveness and adoption of purely black-box algorithmic DHIs. In this paper, we address these challenges by developing a pipeline for learning explainable treatment policies for RPM-enabled DHIs. We apply our approach in the real-world setting of RPM using a DHI to improve glycemic control of youth with type 1 diabetes. Our main contribution is to reveal the importance of clinical domain knowledge in developing state and action representations for effective, efficient, and interpretable targeting policies. We observe that policies learned from clinician-informed representations are significantly more efficacious and efficient than policies learned from black-box representations. This work emphasizes the importance of collaboration between ML researchers and clinicians for developing effective DHIs in the real world.
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