基于使用机器学习算法的四个出生体重标准,在出生时对妊娠年龄小的产前预测
Qiu-Yan Yu1,2, Ying Lin3, Yu-Run Zhou3
1Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
Frontiers in artificial intelligence
|January 28, 2026
概括
预测出生时的妊娠年龄小 (SGA) 对于及时干预至关重要. 机器学习模型和逻辑回归在使用各种出生体重标准预测SGA方面表现相似,有助于产前护理.
科学领域:
- 产科和妇科 产科和妇科
- 围产儿医学 围产儿医学
- 医疗保健中的机器学习
背景情况:
- 准确的产前预测对于妊娠年龄小 (SGA) 对于有效的干预至关重要.
- 这项研究评估了机器学习 (ML) 模型,用于在中国怀孕中预测SGA.
- 使用中国和国际出生体重标准进行比较.
研究的目的:
- 评估ML模型在出生时SGA的预测性能.
- 为了比较SGA分类的不同出生体重标准 (中国,Intergrowth 21st,FMF,GROW).
- 在各种模型和标准中确定SGA的关键预测因素.
主要方法:
- 利用了来自中国350,135例单胎怀孕的多式,纵向产前监测数据.
- 开发了ML模型 (CatBoost,XGBoost,LightGBM,ANN,随机森林,堆叠组合) 和后勤回归.
- 采用了拉索回归来选择特征,并在三个怀孕间隔的测试数据集上验证了模型.
主要成果:
- 在出生体重标准中,SGA率差异很大,FMF患病率最高.
- 晚期怀孕模型 (<37周) 显示了SGA的最佳预测能力.
- 使用FMF标准的随机森林模型在怀孕晚期实现了最高的ROC-AUC (0.79);后勤回归性能与中国和GROW标准相比.
结论:
- 国家和国际标准之间存在SGA分类的显著差异.
- 机器学习模型和逻辑回归都为SGA提供了可比的预测性能.
- 这些发现支持风险分层产前护理和优化资源配置.
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