机器学习预测怀孕中期的宫短部,基于第一季度查期的多模式数据:在高风险人群中的观察研究
Shengyu Wu1, Jiaqi Dong1, Jifan Shi2,3,4
1Department of Obstetrics, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University; Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai 200092, China.
Biomedicines
|September 27, 2025
概括
一个XGBoost模型使用早期怀孕数据准确地预测了中期三个月的短宫. 这允许及时进行干预,以减少自发早产 (sPTB) 的风险.
科学领域:
- 产科和妇科 产科和妇科
- 医疗人工智能 医疗人工智能
- 孕产妇和胎儿医学 孕产妇和胎儿医学
背景情况:
- 第二个三个月的宫短是早产的重要危险因素.
- 目前用于预测宫短短的方法在第一季度缺乏可靠性.
- 需要准确且具有成本效益的早期怀孕预测中期短宫.
研究的目的:
- 开发和验证一种机器学习模型,使用第一季度的临床数据来预测中期的短宫.
- 为了确定三个月中期短宫的关键临床预测因素.
主要方法:
- 招募了1480名具有早产风险因素的孕妇.
- 宫长度在20-24周被评估;短宫定义为<25毫米.
- 训练了七个机器学习模型,XGBoost因其性能而被选择并使用SHAP值进行分析.
主要成果:
- 25.4%的参与者在三月中旬出现了短宫.
- XGBoost模型在训练,测试和独立数据集中显示出高预测准确度.
- 关键预测因素包括怀孕前的BMI,怀孕流产史,白细胞计数和阴道微生物群状况.
结论:
- XGBoost模型从第一季度的数据准确地预测了中期的短宫.
- 这为标准评估前的干预提供了6周的窗口.
- 早期预测可以指导预防措施,以减少自发早产风险.
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