预产不良症的机器学习预测模型:系统审查和元分析
Lu Liu1,2, Qixuan Zhu3, Yichi Zong2
1School of Public Health, China Medical University, Shenyang City, Liaoning Province, China.
Journal of medical Internet research
|January 19, 2026
概括
机器学习模型在内部预测妊娠前方面表现出很高的表现,但外部验证显示出显著的变化. 未来的研究需要多中心的外部验证,以确保可靠的临床使用.
科学领域:
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
- 产科 产科 产科 产科 产科
背景情况:
- 孕前是一种严重的高血压疾病,其全球发病率正在增加.
- 现有的预产前预测机器学习 (ML) 模型具有很高的异质性和不清楚的外部可转移性.
- 内部模型性能和现实世界的临床有效性之间的区别仍然是一个关键的差距.
研究的目的:
- 系统地审查和元分析ML模型在预测孕前方面的表现.
- 评估这些ML模型的临床应用价值.
- 确定影响模型性能的因素,并指导未来的研究质量.
主要方法:
- 按照PRISMA指南进行系统审查和元分析.
- 搜索的主要数据库 (PubMed,科学网络,IEEE Xplore,CNKI) 至2025年2月.
- 使用PROBAST评估偏差,用随机效应模型计算总结估计,并计算出95%的预测间隔 (PI).
主要成果:
- 包括31个ML模型的26项研究;汇总的AUC为0.91 (95%CI为0.87-0.92),但具有极端的异质性 (I2>99%).
- 广泛的95%PI灵敏度 (0.32-0.96) 表明外部环境中的潜在性能下降.
- 外部验证 (6项研究) 显示聚合灵敏度较低 (0.68,PI 0.25-0.94). 使用实验室生物标志物和神经网络的模型显示出有希望的结果.
结论:
- 在ML模型中的高内部AUC可能不会转化为普遍的临床有效性.
- 模型性能高度依赖上下文,强调需要外部验证.
- 未来的研究必须优先考虑多中心,未来的外部验证和重新校准,以提高可转移性和可靠性.
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