使用基于电子健康记录的机器学习预测不良药物事件:系统性审查和元分析
Qiaozhi Hu1,2, Yuxian Chen1, Dan Zou1
1Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
机器学习 (ML) 有效地使用电子健康记录 (EHR) 预测药物不良事件 (ADEs). 本综述综合了59项研究,突出了随机森林作为ADE预测的关键算法,证明了强大的模型性能.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 临床药理学 临床药理学
背景情况:
- 药物不良事件 (ADEs) 是一个重大的临床挑战.
- 机器学习 (ML) 为使用电子健康记录 (EHR) 数据预测ADEs提供了一个有希望的方法.
研究的目的:
- 系统地审查ML在基于EHR数据预测特定ADEs中的应用.
- 为 ML 模型,算法及其在 ADE 预测中的性能提供全面的概述.
主要方法:
- 在主要数据库 (PubMed,科学网,Embase,IEEE Xplore) 进行了系统的文献搜索,截至2024年5月20日.
- 包括开发ML模型的研究,用于使用EHR数据预测特定的ADE或与药物相关的ADE.
主要成果:
- 包括59项研究,重点关注15种药物和15种ADEs. 随机森林是最常见的算法,其次是SVM,XGBoost,DT和LightGBM.
- ML模型的表现普遍强,平均AUC为76.68%,准确率为76.00%. 总结 SROC 分析得出 AUC 为 0.8069.
结论:
- 机器学习模型显示出从EHR数据中预测ADEs的巨大潜力.
- 未来的研究应该优先考虑标准化,多中心研究与多样化的数据,和现实世界的临床影响评估.
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