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Updated: Jan 11, 2026

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基于XGBoost的分析与自发早产相关的孕产妇和生化因素:一个回顾性队列研究

Ying Gao^1,2, Xiaoqin Huang², Caihua Tan²

¹Affiliated Shenzhen Women and Children's Hospital (Longgang) of Shantou University Medical College (Longgang District Maternity & Child Healthcare Hospital of Shenzhen City), Shenzhen, China.

BMC pregnancy and childbirth

|November 19, 2025

概括

自发早产 (sPTB) 的预测很差. 这项研究确定了AFP和BMI等风险因素,但模型表现不佳,这表明需要更好的指标.

科学领域:

产科和妇科产科和妇科
孕产妇和胎儿医学孕产妇和胎儿医学
生物标志物研究生物标志物研究

背景情况:

自发早产 (sPTB) 是新生儿发病的主要原因.
目前对sPTB的早期风险评估是不充分的.
确定sPTB可靠的预测因子至关重要.

研究的目的:

评估血清生物标志物和母亲因子与sPTB的关联.
评估这些因素对sPTB的预测潜力.
开发和比较sPTB的预测模型.

主要方法:

19,818例怀孕 (2020-2024) 的回顾性队列研究.
分析包括母亲的特征,健康状况和血清生物标志物 (PAPP-A,AFP,β-hCG,UE3).
通过LASSO回归选择特征;使用XGBoost和物流回归的预测模型.

主要成果:

653名参与者 (3.29%) 经历了sPTB.
确定了六个重要的因素:PAPP-A (保护性),AFP,BMI,剖腹产史,高血压,宫无能 (危险因素).
在XGBoost和物流回归模型中,预测性能有限 (AUC从0.588到0.703不等).

结论:

AFP和宫不称职是sPTB的关键预测因素.
像BMI,剖腹产史,高血压和PAPP-A水平这样的孕产妇因素也产生了影响.
目前的模型显示sPTB预测很弱;未来的研究应该探索更强大的指标.

关键词:

阿尔法胎儿蛋白质是一种阿尔法胎儿蛋白质.机器学习机器学习血蛋白是一种预测模型是一个预测模型.自发的早产,即早产.

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