强化学习是否能改善重症患者的治疗结果? 一个系统的审查和准备程度评估
Martijn Otten1,2, Ameet R Jagesar1,2, Tariq A Dam1,2
1Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence, Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
Critical care medicine
|November 8, 2023
概括
强化学习 (RL) 对重症监护病房 (ICU) 决策有很大的希望. 然而,目前的研究虽然表明RL优于临床医生,但缺乏通过潜在患者结果数据的验证.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 密集护理医学 密集护理医学
背景情况:
- 强化学习 (RL) 是一种机器学习技术,擅长进行顺序决策.
- 目前正在探索其在重症监护室 (ICU) 用于关键病人的管理中的应用.
- 这项研究系统地审查RL的准备和对重症患者的影响.
研究的目的:
- 系统地审查和评估强化学习 (RL) 应用在重症监护中的准备程度.
- 对RL对重症监护机构患者结果的影响进行元分析.
- 评估重症监护医学的RL实施现状.
主要方法:
- 在主要的科学数据库 (PubMed,Embase,Web of Science,SCOPUS,IEEE Xplore) 进行了全面的系统搜索,截至2022年3月25日.
- 包括关于ICU人群中RL的期刊文章,报告患者的结果和准备评估的会议论文.
- 数据提取侧重于模型特征,结果比较和偏差风险评估.
主要成果:
- 选了1033篇文章,其中包括18篇期刊文章和18篇会议论文.
- 所有包括的研究都报告RL算法表现优于临床决策,但仅基于追溯数据.
- 观察到RL建模技术的显著差异和偏差的高整体风险,主要是由于评估方法.
结论:
- 这次系统性审查没有发现任何研究表明使用基于RL的技术在重症监护中改善了患者的治疗结果.
- 虽然在所有审查的研究中,RL政策的表现优于临床医生的政策,但这些发现仅限于追溯的政策之外的评估.
- 需要进一步的研究来验证RL在重症监护机构的临床实用性和患者益处.
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