Reinforcement Learning in Neurocritical and Neurosurgical Care: Principles and Possible Applications
Open Access
- 22 February 2021
- journal article
- review article
- Published by Hindawi Limited in Computational and Mathematical Methods in Medicine
- Vol. 2021, 1-6
- https://doi.org/10.1155/2021/6657119
Abstract
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 RLs 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.Keywords
Funding Information
- Science and Technology Commission of Shanghai Municipality (17YF1426700, 82073640)
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