机器学习用于分析疾病预测和管理中的真实世界数据的使用:系统审查
Norah Hamad Alhumaidi1, Doni Dermawan2, Hanin Farhana Kamaruzaman3,4
1College of Medicine, Qassim University, Buraidah, Saudi Arabia.
JMIR medical informatics
|June 19, 2025
概括
机器学习 (ML) 和大数据分析显示,使用真实世界数据 (RWD) 进行疾病预测和管理具有很大的前景. 解决数据质量和模型透明度方面的挑战对于广泛的临床采用至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 数据科学在医学中的数据科学
背景情况:
- 机器学习 (ML) 和大数据分析正在彻底改变医疗保健,特别是在疾病预测,管理和个性化护理方面.
- 来自EHR,注册表和可穿戴设备等来源的真实世界数据 (RWD) 为ML应用提供了巨大的潜力,以改善临床结果.
- 关键的挑战包括数据质量,模型透明度,概括性和无集成到临床工作流程中.
研究的目的:
- 系统地审查ML在分析RWD中的应用,以预测和管理疾病.
- 确定当前研究中使用的流行ML方法,疾病类型,研究设计和RWD来源.
- 探索医疗保健中现有的ML实践的优点和局限性.
主要方法:
- 按照PRISMA指南进行了系统的文献搜索.
- 包括在2014年至2024年期间发表的使用ML用于RWD分析在疾病预测/管理中的研究.
- 提取的数据包括ML算法,疾病类别,研究设计和RWD来源 (EHR,注册表,可穿戴设备).
主要成果:
- 分析了57项涉及超过15万名患者的研究.
- 随机森林 (42%),后勤回归 (37%) 和SVM (32%) 是最常见的ML方法.
- 心血管疾病 (33%),癌症 (16%),神经疾病 (11%) 是主要关注的领域,电子健康记录是主要的数据来源.
- ML模型在改善临床决策,患者分层和治疗优化方面表现有前途,例如心血管预测的AUC为0.85.
- 然而,60%的研究报告了数据质量,模型解释性和通用性方面的挑战.
结论:
- 机器学习和大数据分析具有在医疗保健中推进疾病预测和管理的巨大潜力.
- 处理数据质量,模型透明度和通用性对于实现这些技术的全部好处至关重要.
- 未来的研究应该优先解决这些挑战,以促进更广泛的临床实施和改善患者的治疗结果.
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