对非传染性疾病干预中强化学习的系统文献综述
Yanfeng Zhao1, Jun Kit Chaw1, Lin Liu2
1Institute of Visual Informatics, National University of Malaysia, Bangi, Selangor, Malaysia.
Artificial intelligence in medicine
|June 5, 2024
概括
强化学习 (RL) 显示出通过调整干预措施来治疗非传染性疾病 (NCD) 的前景. 本综述分析了RL算法,并确定了临床使用的安全性和可解释性等挑战.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 公共卫生 公共卫生
背景情况:
- 非传染性疾病 (NCDs) 导致严重的死亡率和经济损失.
- 机器学习 (ML),特别是强化学习 (RL),为NCD提供了动态干预策略.
- RL能够适应疾病进展和长期目标的能力使其成为NCD管理的前景.
研究的目的:
- 对NCD干预措施的首选RL算法,数据源,设计细节和临床应用障碍进行审查.
- 促进RL在NCD干预研究中的早期临床应用.
- 为了提供一个全面的概述当前的景观和未来的方向RL在NCDs.
主要方法:
- 使用PRISMA流程图对40篇相关论文进行了系统审查.
- 对所选的研究进行了定量和定性分析.
- 综合了有关RL算法,数据和临床挑战的现有研究.
主要成果:
- 深度Q网络 (DQN) 和Actor-Critic算法及其变体通常用于对回顾性数据的RL模型进行训练.
- 患者的身体状况定义了状态空间,干预定义了行动空间,条件的变化提供了即时的回报.
- 为了应对临床应用的挑战,有几种方法存在,但没有建立通用解决方案.
结论:
- RL算法对个性化NCD干预具有显著的潜力.
- 临床实施的关键挑战包括安全性,可解释性,培训效率和勘探/开发平衡.
- 需要进一步的研究来开发全面的解决方案,以便在非传染性疾病的临床实践中广泛采用RL.
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