Reinforcement Learning in Healthcare: A Survey
Reinforcement Learning in Healthcare: A Survey
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医疗保健中的强化学习：一项调查
DOI：
10.1145/3477600
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发表时间：
2023-01-01
影响因子：
16.6
通讯作者：
Yin, Guosheng
中科院分区：
文献类型：
--
作者：
Yu, Chao;Liu, Jiming;Yin, Guosheng
作为机器学习的一个子领域，强化学习(RL)旨在利用主体与其环境的交互样本和潜在的延迟反馈来优化决策。与传统的监督学习不同，传统的监督学习通常依赖于一次性、穷举和监督的奖励信号，RL同时通过采样、评估和延迟反馈来处理顺序决策问题。这种独特的特征使RL技术成为在各种医疗领域开发强大解决方案的合适候选者，在这些领域，诊断决策或治疗方案通常具有延迟反馈的较长时间的特点。通过首先简要介绍RL研究的理论基础和关键方法，本调查提供了RL在各种医疗领域的广泛应用概述，范围从慢性病和危重护理的动态治疗机制、自动化医疗诊断以及渗透到医疗系统各个方面的许多其他控制或调度问题。此外，我们讨论了当前研究中的挑战和有待解决的问题，并强调了一些潜在的解决方案和未来研究的方向。
As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision making by using interaction samples of an agent with its environment and the potentially delayed feedbacks. In contrast to traditional supervised learning that typically relies on one-shot, exhaustive, and supervised reward signals, RL tackles sequential decision-making problems with sampled, evaluative, and delayed feedbacks simultaneously. Such a distinctive feature makes RL techniques a suitable candidate for developing powerful solutions in various healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged period with delayed feedbacks. By first briefly examining theoretical foundations and key methods in RL research, this survey provides an extensive overview of RL applications in a variety of healthcare domains, ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis, and many other control or scheduling problems that have infiltrated every aspect of the healthcare system. In addition, we discuss the challenges and open issues in the current research and highlight some potential solutions and directions for future research.