The rapid increase in the percentage of chronic disease patients along with the recent pandemic pose immediate threats on healthcare expenditure and elevate causes of death. This calls for transforming healthcare systems away from one-on-one patient treatment into intelligent health systems, to improve services, access and scalability, while reducing costs. Reinforcement Learning (RL) has witnessed an intrinsic breakthrough in solving a variety of complex problems for diverse applications and services. Thus, we conduct in this paper a comprehensive survey of the recent models and techniques of RL that have been developed/used for supporting Intelligent-healthcare (I-health) systems. This paper can guide the readers to deeply understand the state-of-the-art regarding the use of RL in the context of I-health. Specifically, we first present an overview for the I-health systems challenges, architecture, and how RL can benefit these systems. We then review the background and mathematical modeling of different RL, Deep RL (DRL), and multi-agent RL models. After that, we provide a deep literature review for the applications of RL in I-health systems. In particular, three main areas have been tackled, i.e., edge intelligence, smart core network, and dynamic treatment regimes. Finally, we highlight emerging challenges and outline future research directions in driving the future success of RL in I-health systems, which opens the door for exploring some interesting and unsolved problems.
翻译:慢性病患者比例的迅速增加以及最近的大流行病,对保健支出和死亡原因的提高构成直接威胁。这要求将保健系统从一对一的病人治疗转变为智能保健系统,改善服务、获得和可扩缩性,同时降低成本。强化学习(RL)在解决多种应用和服务的各种复杂问题方面取得了内在突破。因此,我们在本文件中对为支持智能保健(I-Health)系统而开发/使用的RL(I-Health)系统而开发/使用的RL(I-Health)的最近模式和技术进行了全面调查。本文件可以指导读者深入了解在I-Helth范围内使用RL(R-L)方面的最新最新知识。具体地说,我们首先概述了I-Hel系统的挑战、结构以及RL(R)如何使这些系统受益。然后我们审查了不同的RL、Deep RL(DL)和多剂RL(DL)模型的背景和数学模型。随后,我们为I-H(I-H)系统的应用提供了深入的文献审查。特别是三个主要领域,即智能-L(R-L)未来研究的前沿、核心网络和正在探索的系统。