基于机器学习的确定孕妇脉压的决定因素
1Department of Statistics, Salale University, Fiche, Ethiopia.
Global epidemiology
|January 22, 2026
概括
机器学习模型准确地预测孕妇的脉压 (PP),识别关键因素,如产妇年龄和妊娠年龄. 这有助于更好地了解怀孕期间的动脉硬和心血管风险,特别是在资源较少的环境中.
科学领域:
- 心血管研究的心血管研究.
- 孕产妇健康 孕产妇健康
- 机器学习在医疗保健中的应用
背景情况:
- 脉压 (PP) 是动脉硬和怀孕期间心血管风险的关键指标.
- 对PP的纵向决定因素尚不清楚,特别是在资源较少的环境中.
研究的目的:
- 在孕妇中确定纵向脉冲压 (PP) 的决定因素.
- 将机器学习 (ML) 预测性能与传统的混合效应模型进行比较.
主要方法:
- 埃塞俄比亚549名孕妇的回顾性队列研究,共计2760次PP测量.
- 应用了通用线性混合模型,随机森林和XGBoost回归.
- 使用RMSE和MAE评估预测性表现.
主要成果:
- 基于树的ML模型在预测PP方面表现优于混合效应模型.
- 在预测因素和PP之间观察到非线性和时间依赖的关系.
- 母亲年龄,体重,妊娠年龄和先前的PP值是关键预测因素.
结论:
- ML提供了一个灵活和有效的框架来建模妊娠中的纵向PP动态.
- 这种方法提高了对临床和时间PP决定因素的理解.
- ML可以支持在孕产妇医疗保健中改善心血管风险评估.
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