Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions

IF 1.7 3区 数学 Q1 STATISTICS & PROBABILITY
Nina Deliu, Joseph Jay Williams, Bibhas Chakraborty
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引用次数: 0

Abstract

SummaryIn 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.
现代生物统计学中的强化学习:构建最佳自适应干预措施
摘要 近年来,强化学习(RL)在与健康相关的顺序决策问题中占据了重要地位,作为提供适应性干预(AIs)的重要工具而备受关注。然而,部分由于方法论界和应用界之间的协同作用不佳,其在现实生活中的应用仍然有限,其潜力仍有待发挥。为了弥补这一不足,我们的工作首次提供了统一的 RL 方法技术调查,并辅以案例研究,用于构建医疗保健领域的各类人工智能。特别是,我们利用 RL 这一共同的方法论保护伞,将移动医疗中的动态治疗方案和及时自适应干预这两个看似不同的人工智能领域联系起来,强调了它们之间的异同,并讨论了使用 RL 的意义。我们还概述了有待解决的问题以及未来研究方向的考虑因素。最后,我们利用在这两个领域设计案例研究的经验,展示了统计、RL 和医疗保健研究人员在推进人工智能方面的重要合作机会。
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来源期刊
International Statistical Review
International Statistical Review 数学-统计学与概率论
CiteScore
4.30
自引率
5.00%
发文量
52
审稿时长
>12 weeks
期刊介绍: International Statistical Review is the flagship journal of the International Statistical Institute (ISI) and of its family of Associations. It publishes papers of broad and general interest in statistics and probability. The term Review is to be interpreted broadly. The types of papers that are suitable for publication include (but are not limited to) the following: reviews/surveys of significant developments in theory, methodology, statistical computing and graphics, statistical education, and application areas; tutorials on important topics; expository papers on emerging areas of research or application; papers describing new developments and/or challenges in relevant areas; papers addressing foundational issues; papers on the history of statistics and probability; white papers on topics of importance to the profession or society; and historical assessment of seminal papers in the field and their impact.
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