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将机器学习集成到疾病风险预测建模中的统计方法中:系统性审查
Meng Zhang1,2, Yongqi Zheng1,2, Xiagela Maidaiti3
1Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
Health data science
|July 25, 2024
概括
将机器学习与统计方法相结合,可以创建更强大的疾病预测模型. 这些综合方法显示出超越单一方法的潜力,为诊断和预后提供更高的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 生物统计学 生物统计学
背景情况:
- 疾病预测模型通常仅依赖统计方法或机器学习,增加错误风险.
- 将机器学习集成到统计方法中可能会提高预测模型的稳定性.
- 综合疾病预测模型的当前全球发展需要全面评估.
研究的目的:
- 系统地审查和评估全球疾病预测模型的发展,这些模型将机器学习与统计方法集成在一起.
- 确定共同的集成策略,应用场景和性能指标.
主要方法:
- 在多个数据库 (PubMed, EMbase, Web of Science, CNKI, VIP, WanFang, SinoMed) 进行了系统的文献搜索,截至2023年5月.
- 包括专注于整合机器学习和统计方法的预测模型的研究.
- 提取的数据包括研究特征,集成方法,应用领域,建模细节和性能.
主要成果:
- 包括21项研究 (20英语,1中文),重点是诊断 (5) 和预后/事件预测 (16) 模型.
- 整合策略包括投票,堆叠和模型选择用于分类,以及统计组合用于回归.
- 与单一方法相比,集成模型在大多数情况下显示出更高的性能 (AUROC>0.75),堆叠适合高预测率场景.
结论:
- 整合机器学习与疾病预测的统计方法的研究正在出现,但显示出显著的潜力.
- 综合模型可以优于个别的统计或机器学习方法.
- 本综述为选择整合方法提供了指导,并强调需要对策略改进和验证进行进一步研究.
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