在抗癌药物治疗中精确剂量的机器学习方法:范围审查
Olga Teplytska1, Moritz Ernst2, Luca Marie Koltermann1
1Department of Clinical Pharmacy, Institute of Pharmacy, University of Bonn, An der Immenburg 4, 53121, Bonn, Germany.
机器学习 (ML) 在优化抗癌药物剂量方面表现有前途. 这次审查发现了经常使用的强化学习方法,突出了ML.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 药理学 药理学是指药理学的学科.
背景情况:
- 机器学习 (ML) 技术已经出现,用于个性化抗癌药物剂量.
- 方法通常依赖于假定的药物效应或测量的生物标志物.
- 在癌症治疗中,精确剂量是研究的一个关键领域.
研究的目的:
- 综合总结ML在抗癌药物的精确剂量研究中的研究状况.
- 审查2016年以后发表的研究.
主要方法:
- 根据科克莱恩和乔安娜·布里格斯研究所的指导方针进行了范围审查.
- 进行了对Medline,Embase和Cochrane图书馆数据库的系统搜索.
- 报告遵守了PRISMA-ScR检查清单.
主要成果:
- 确定了17项相关研究.
- 在12项研究中使用了强化学习 (RL) 方法,包括各种Q-学习算法.
- 经典的ML方法进行了比较,AI平台在有限的患者中指导了剂量.
结论:
- ML方法在优化抗癌药物剂量方面具有临床意义.
- 许多ML算法展示了无模型预测的潜力.
- 与标准方案相比,ML方法可以最大限度地提高疗效并最大限度地降低毒性.
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