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开发和外部验证一个集成社交网络变量的非侵入性早期妊娠糖尿病预测模型:基于机器学习的前性队列研究

Qianqian Li^1,2, Yalin Tang³, Xiuling Yang²

¹Department of Nursing, The Affiliated Hospital of Qingdao University, Qingdao, China.

BMC pregnancy and childbirth

|December 30, 2025

概括

这项研究开发了一种机器学习模型,用于预测妊娠糖尿病 (GDM),并结合了社交网络因素. 该XGBoost模型显示高准确度,改善风险分层超出传统预测器.

科学领域:

生殖健康生殖健康
机器学习在医学中的应用
公共卫生公共卫生

背景情况:

目前的妊娠糖尿病 (GDM) 预测模型往往忽视了社交网络的影响.
整合社会因素可以提高早期GDM风险评估的准确性.

研究的目的:

使用机器学习 (ML) 开发和验证早期GDM预测模型.
整合社交网络特征与传统的非侵入性预测器,以改善GDM预测.

主要方法:

一项前性队列研究,涉及2433名孕妇.
在22个选风险因素上训练了7个ML算法 (包括XGBoost).
使用30次重复分层10倍交叉验证和外部验证与ROC曲线,校准曲线和DCA.

主要成果:

XGBoost模型在开发 (AUC=0.980) 和外部验证 (AUC=0.901) 中实现了最佳性能.
社会网络特征 (网络规模,联系频率) 被确定为重要的预测因素.
该模型在决策曲线分析中显示出良好的校准和优越的净收益.

结论:

基于ML的GDM预测模型集成社交网络变量显示高性能和强大的外部验证.
与传统因素相比,社交网络指标显著改善了GDM风险分层.
这种方法为开发更准确,更稳定的GDM预测模型提供了一种新方法.

关键词:

早期预测预测的早期预测孕期糖尿病是一种糖尿病.机器学习机器学习孕产妇和婴儿的医疗保健.社交网络社交网络

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