预测静脉血栓栓塞的机器学习:系统审查和元分析
Ruyi Ma1,2, Weifeng Yu3, Jian Tian2
1School of Nursing, Jilin University, Changchun, China.
Journal of medical Internet research
|December 23, 2025
概括
机器学习 (ML) 模型在预测静脉血栓塞栓症 (VTE) 中表现有前途,达到0.79的综合灵敏度和0.82.8的特异性. 然而,大多数研究都有很高的偏差风险,需要提高临床应用的方法严格性.
科学领域:
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
- 临床预测模型临床预测模型
背景情况:
- 机器学习 (ML) 模型越来越多地用于静脉血栓栓塞 (VTE) 风险预测.
- 这些ML模型的质量和实际应用性需要彻底调查.
- 关键的研究领域包括ML预测机制和确定VTE的关键预测因素.
研究的目的:
- 系统地审查和评估VTE的ML模型的预测性能.
- 评估基于ML的VTE风险模型的整体诊断准确性和确定关键预测因素.
主要方法:
- 在主要数据库中进行了全面的文献搜索,截至2025年3月26日.
- 包括开发和验证患者群体VTE预测ML模型的研究.
- 进行了元分析以确定聚合的灵敏度,特异性和C指数,并使用标准化工具评估偏差风险.
主要成果:
- 分析了27项涉及596,092名患者的研究.
- 聚合灵敏度为0.79和聚合特异性为0.82,C指数为0.84.
- 在67%的研究中发现了偏差的高风险;年龄,D-二次数和VTE史是显著的预测因素.
结论:
- ML模型显示了在患者中预测VTE的显著潜力.
- 现有研究中偏差的高普遍性,特别是在数据处理和设计报告中,限制了当前的适用性.
- 未来的研究应该专注于外部验证和加强方法论的严谨性,以实现临床翻译.
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