论文标题
现代生物统计学中的强化学习：构建最佳自适应干预措施
Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions
论文作者
论文摘要
近年来，加强学习（RL）在与健康相关的顺序决策问题中获得了突出的立场，成为提供适应性干预措施（AIS）的宝贵工具的吸引力。但是，部分由于方法论和应用社区之间的协同作用差，其现实生活中的应用仍然有限，并且其潜力仍有待实现。为了解决这一差距，我们的工作提供了有关RL方法的首次统一技术调查，并补充了案例研究，用于在医疗保健中构建各种AIS。特别是，使用RL的常见方法论，我们在移动健康中桥接了两个看似不同的AI领域，动态治疗方案和即时自适应干预措施，突出了它们之间的相似性和差异，并讨论了使用RL的含义。概述了未来研究方向的开放问题和考虑因素。最后，我们利用我们在两个领域设计案例研究方面的经验，展示了统计，RL和医疗保健研究人员在推进AIS方面的重要协作机会。
In recent years, reinforcement learning (RL) has acquired a prominent position in health-related sequential decision-making problems, gaining traction as a valuable tool for delivering adaptive interventions (AIs). However, in part due to a poor synergy between the methodological and the applied communities, its real-life application is still limited and its potential is still to be realized. To address this gap, our work provides the first unified technical survey on RL methods, complemented with case studies, for constructing various types of AIs in healthcare. In particular, using the common methodological umbrella of RL, we bridge two seemingly different AI domains, dynamic treatment regimes and just-in-time adaptive interventions in mobile health, highlighting similarities and differences between them and discussing the implications of using RL. Open problems and considerations for future research directions are outlined. Finally, we leverage our experience in designing case studies in both areas to showcase the significant collaborative opportunities between statistical, RL, and healthcare researchers in advancing AIs.
