作者
Shounak Datta, Yanjun Li, Matthew M Ruppert, Yuanfang Ren, Benjamin Shickel, Tezcan Ozrazgat-Baslanti, Parisa Rashidi, Azra Bihorac
发表日期
2021/7/1
来源
Surgery
卷号
170
期号
1
页码范围
329-332
出版商
Mosby
简介
Patients and physicians make essential decisions regarding diagnostic and therapeutic interventions. These actions should be performed or deferred under time constraints and uncertainty regarding patients’ diagnoses and predicted response to treatment. This may lead to cognitive and judgment errors. Reinforcement learning is a subfield of machine learning that identifies a sequence of actions to increase the probability of achieving a predetermined goal. Reinforcement learning has the potential to assist in surgical decision making by recommending actions at predefined intervals and its ability to utilize complex input data, including text, image, and temporal data, in the decision-making process. The algorithm mimics a human trial-and-error learning process to calculate optimum recommendation policies. The article provides insight regarding challenges in the development and application of reinforcement …
引用总数
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S Datta, Y Li, MM Ruppert, Y Ren, B Shickel… - Surgery, 2021