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.

翻译：近年来，强化学习在健康相关序贯决策问题中占据重要地位，已成为提供自适应干预的有效工具。然而，部分由于方法学界与应用研究界之间的协同不足，其实际应用仍十分有限，潜力尚未充分释放。为弥补这一差距，本文首次系统性综述了强化学习方法，并结合案例研究，探讨其在医疗健康领域构建各类自适应干预中的应用。具体而言，我们以强化学习作为统一方法论框架，桥接了两个看似不同的自适应干预领域——动态治疗方案与移动健康中的即时自适应干预，阐明了两者的共性与差异，并讨论了使用强化学习的潜在影响。同时，本文概述了当前存在的开放性问题及未来研究方向。最后，我们基于在两个领域设计案例研究的实践经验，展示了统计学、强化学习与医疗健康研究人员在推进自适应干预方面的重要合作机遇。