Lhorong People's Hospital, Tibet, China.
Department of Neurosurgery, Huashan Hospital, Shanghai Medical School, Fudan University, Shanghai, China.
Comput Math Methods Med. 2021 Feb 22;2021:6657119. doi: 10.1155/2021/6657119. eCollection 2021.
Dynamic decision-making was essential in the clinical care of surgical patients. Reinforcement learning (RL) algorithm is a computational method to find sequential optimal decisions among multiple suboptimal options. This review is aimed at introducing RL's basic concepts, including three basic components: the state, the action, and the reward. Most medical studies using reinforcement learning methods were trained on a fixed observational dataset. This paper also reviews the literature of existing practical applications using reinforcement learning methods, which can be further categorized as a statistical RL study and a computational RL study. The review proposes several potential aspects where reinforcement learning can be applied in neurocritical and neurosurgical care. These include sequential treatment strategies of intracranial tumors and traumatic brain injury and intraoperative endoscope motion control. Several limitations of reinforcement learning are representations of basic components, the positivity violation, and validation methods.
动态决策在外科患者的临床护理中至关重要。强化学习 (RL) 算法是一种在多个次优选项中找到最优序列决策的计算方法。本综述旨在介绍 RL 的基本概念，包括三个基本组成部分：状态、动作和奖励。大多数使用强化学习方法的医学研究都是在固定的观察数据集上进行训练的。本文还回顾了使用强化学习方法的现有实际应用的文献，可以进一步分为统计 RL 研究和计算 RL 研究。综述提出了强化学习在神经危重病和神经外科护理中应用的几个潜在方面。这些方面包括颅内肿瘤和创伤性脑损伤的序贯治疗策略以及术中内窥镜运动控制。强化学习的几个局限性包括基本组件的表示、正性违反和验证方法。
