使用机器学习预测孕妇自发早产的情况
Xiaoxue Yang1, Xuewu Song2, Kun Yang1
1Ultrasonic Diagnosis Center, Northwest Women's and Children's Hospital, No. 1616, Yanxiang Rd, Xi'an, 710061, Shaanxi, China.
Archives of gynecology and obstetrics
|July 12, 2025
概括
机器学习模型可以预测自发早产 (sPTB) 风险. 关键预测因素包括甲状腺激素和红细胞分布宽度,有助于早期识别改善孕产妇和新生儿的结果.
科学领域:
- 生殖医学 生殖医学
- 计算生物学 计算生物学
- 临床预测建模模型
背景情况:
- 在全球范围内,自发早产 (sPTB) 是造成母亲和新生儿不良后果的主要原因.
- 早期识别sPTB风险对于及时干预和改善健康结果至关重要.
- 目前用于sPTB风险评估的方法存在局限性,需要新的方法.
研究的目的:
- 调查使用机器学习 (ML) 算法用于预测自发早产 (sPTB) 风险的可行性.
- 通过基于机器学习的分析来识别导致sPTB风险的关键变量.
主要方法:
- 对1122名孕妇进行了回顾性数据收集 (187名sPTB,935名产期妇女).
- 使用八种ML算法和六种变量选择方法开发和评估预测模型.
- 通过AUROC,AUPRC进行绩效评估,准确度,灵敏度,F1得分,PPV和NPV.
主要成果:
- 性能最好的模型结合了分类增强算法和后向消除,实现了AUROC为0.8762和AUPRC为0.7061.
- 最好的模型的Brier分数在测试组上为0.12.
- 对于sPTB风险的顶级预测因素包括自由的三甲状腺素,白蛋白/血球蛋白比率,甲状腺蛋白抗体,总甲状腺素和红细胞体积分布宽度.
结论:
- ML模型在识别高风险sPTB的孕妇方面表现有前途.
- 个体风险因素分析可以为临床决策提供信息.
- 需要进行进一步的研究,以提高该模型的通用性和临床适用性,因为它依赖于非例行实验室测试.
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