机器学习用于预测中风风险分层使用多组学数据:系统审查
Hae Young Yoo1, Hyerim Shin1, Eun-Jung Kim1
1Chung-Ang University, Seoul, Republic of Korea.
Journal of medical Internet research
|February 19, 2026
概括
多种机器学习 (ML) 模型对中风风险预测有前途,但目前的研究存在方法上的局限性. 需要改进验证和报告,以便在精确性中风护理中临床应用.
科学领域:
- 综合生物信息学和计算生物学
- 翻译性中风研究研究
- 精准医学和人工智能
背景情况:
- 脑卒中是一种复杂的疾病,涉及多种生物途径.
- 单个主题的方法是不够的;多个主题的数据提供了更深入的见解,但也带来了分析挑战.
- 机器学习 (ML) 可以解决多omics复杂性,但其在中风中的预测准确性和可重现性未得到充分探索.
研究的目的:
- 系统地审查使用多omics数据进行中风风险分层的ML模型.
- 分析歧视性绩效,数据整合策略和验证/报告实践.
- 为了指导未来的方法学进步在多omicsML中风.
主要方法:
- 在9个数据库 (2000年1月至2025年7月) 进行系统的文献搜索 (PRISMA 2020).
- 纳入标准:成年人,中风预测,≥2个omics层,报告的ML性能指标.
- 评估了偏差风险 (PM-ROB) 和报告质量 (MIMAR);主要结果:接收器操作特征曲线 (AUC) 下的面积.
主要成果:
- 7项研究 (n=40,274) 符合标准,发表于2022-2025年,整合了2个omics层 (例如,代谢学-蛋白质学).
- 监督的ML算法包括SVM,树组,GLM和深度学习.
- 报告了高明显的歧视 (AUC 0.75-0.97),但只有3项研究进行了外部验证;校准和操作点很少被评估.
结论:
- 多omics ML模型显示了明显高的中风风险分层性能,但面临着方法上的局限性.
- 小样本大小,设计异质性和不完整的报告阻碍了可重现性和可概括性.
- 未来的研究需要强大的评估,外部验证和基准测试,以确定精确的中风护理的临床实用性.
关键词:
ML ML 在 ML深度学习是一种深度学习.在表观基因组学上,表观基因组学.基因组学就是基因组学.脂质组的类型是什么机器学习是机器学习.代谢生物组的代谢生物组多种多种多种多种多种多种多种多种多种多种.蛋白质组学 蛋白质组学风险分层的分层是风险分层.一次性中风中风中风中风中风翻译学 翻译学 翻译学 翻译学更多相关视频
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