远程患者监测的自适应控制策略的安全学习
Safe Learning of Adaptive Control Policies for Remote Patient Monitoring
- Indian Institute of Science (IISc)（印度科学学院）
- Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对远程患者监测中系统参数未知时的安全与成本平衡问题，提出一种优先保障患者安全的在线基于模型的强化学习算法，经仿真验证可收敛至最优阈值策略并降低危急健康状态风险。
AI中文摘要：
远程患者监测（Remote Patient Monitoring，RPM）可在患者日常环境中对其进行连续观察，改善健康结局与生活质量。RPM的关键挑战是确定最优监测强度，同时平衡患者安全与监测成本；当系统参数（如转移概率、成本）初始未知时，该问题会进一步复杂化。我们开发了一种基于学习的控制框架，用于估计这些参数并实时调整监测策略，该方法是专为RPM定制的在线基于模型的强化学习算法，探索过程中明确优先考虑患者安全。我们提供了安全性和收敛至最优策略的理论保证；仿真结果显示，该算法收敛至最优阈值型策略，维持较低的治疗成本，并降低患者进入危急健康状态的风险。
英文摘要：
Remote Patient Monitoring (RPM) enables continuous observation of patients in their daily environments, improving both health outcomes and quality of life. A key challenge in RPM is determining the optimal monitoring intensity, while balancing patient safety and monitoring costs. This problem is further complicated when system parameters, such as transition probabilities and costs, are initially unknown. We develop a learning-based control framework that estimates these parameters and adapts the monitoring policy in real time. The proposed approach is an online model-based reinforcement learning algorithm tailored to RPM, with patient safety explicitly prioritized during exploration. We provide theoretical guarantees on safety and convergence to the optimal policy. Simulation results show that the algorithm converges to the optimal threshold-based policy, maintains low treatment costs, and reduces the risk of patients reaching critical health states.
