Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions
这篇综述统一介绍了强化学习方法在医疗健康中构建自适应干预的应用，结合案例研究，连接动态治疗方案和移动健康中的即时自适应干预，并指出开放问题和未来方向，对统计、强化学习和医疗研究者有参考价值。
Summary 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 realised. 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.