现代生物统计学中的强化学习：构建最优自适应干预方案
Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions
AI总结：
本文针对强化学习在医疗自适应干预中应用受限的问题，首次以RL为统一框架综述相关方法，衔接动态治疗方案与移动健康即时自适应干预两大领域，辅以案例并指明合作方向。
摘要：
近年来，强化学习（reinforcement learning, RL）在健康相关的序贯决策问题中占据了重要地位，作为实施自适应干预（adaptive interventions, AIs）的有效工具日益受到关注。然而，部分由于方法学界与应用学界之间协同不足，其在实际场景中的应用仍然有限，潜力尚未得到充分发挥。为填补这一空白，本研究首次针对医疗场景中构建各类AIs的RL方法开展了统一的技术综述，并辅以案例研究。具体而言，我们以RL为共同方法框架，衔接了两个看似不同的AIs领域：动态治疗方案与移动健康中的即时自适应干预，阐明了二者的异同，并讨论了应用RL的影响。本文还概述了待解决的问题与未来研究方向的考量。最后，我们依托在这两个领域设计案例研究的经验，展示了统计学、RL与医疗领域研究者在推进AIs发展方面的重要合作机遇。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
