预测从诊断到分娩的间隔在孕前使用电子健康记录
Xiaotong Yang1, Hailey K Ballard2, Aditya D Mahadevan3,4
1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Nature communications
|April 12, 2025
概括
这项研究介绍了PEDeliveryTime,这是一种深度学习模型,可以预测产前的分娩时间. 它有助于评估分娩的紧迫性,以平衡母亲和胎儿的风险.
科学领域:
- 围产儿医学 围产儿医学
- 医疗保健中的人工智能
- 临床信息学 临床信息学
背景情况:
- 孕前是全球孕产妇和产周死亡的主要原因.
- 目前对孕前的治疗依赖于平衡孕产妇和胎儿的风险,分娩时间是关键因素.
- 目前尚无确定的治疗方法来治疗孕前,这凸显了需要改进预测工具的必要性.
研究的目的:
- 开发和外部验证深度学习模型 (PEDeliveryTime) 来预测从产前诊断到分娩的时间.
- 评估这些模型在优先考虑医疗资源和管理紧急交付方面的临床实用性.
- 提供一个工具,帮助临床医生做出关键的决定,关于分娩时间在产前病例.
主要方法:
- 密歇根大学开发深度学习模型,使用密歇根大学1533例孕前病例的电子健康记录.
- 佛罗里达大学对2172例孕前病例的PEDeliveryTime模型的外部验证.
- 使用c指数指标对完整队列和早期预先怀孕子子集的模型性能评估.
主要成果:
- 完整的PEDeliveryTime模型实现了高预测性能,其c指数为0.79 (密歇根州) 和0.74 (佛罗里达州).
- 对于早期发病的先兆子,完整模型表现出强的表现,c指数为0.76 (密歇根州) 和0.67 (佛罗里达州).
- 这些模型利用了12个特征的有限集合,增强了它们临床实施的潜力.
结论:
- 开发的PEDeliveryTime模型准确地预测了孕前病例的分娩时间.
- 这些模型提供了早期评估的交付紧迫性,潜在地改善资源配置和患者管理.
- 外部验证证实了深度学习方法在孕前管理中的可通用性和临床相关性.
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