机器学习方法的性能用于预测中等收入国家的子宫前
J Torres-Torres1,2, J R Villafan-Bernal3, R J Martinez-Portilla1
1Clinical Research Branch, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City, Mexico.
概括
一个新的机器学习模型使用母亲的数据准确地预测了孕前 (PE) 在头三个月. 这种工具提供了高效和可访问的早期检测,以改善母亲和胎儿的结果.
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
- 医疗保健中的机器学习
背景情况:
- 孕前 (PE) 是一种严重的妊娠并发症,对孕妇和胎儿造成严重后果.
- 现有的PE预测模型具有局限性,特别是在资源有限的环境中.
- 准确和早期预测PE对于及时干预和改善结果至关重要.
研究的目的:
- 开发和验证一种机器学习 (ML) 算法,用于准确地预测妊娠前 (PE) 的第一季度.
- 创建一个高效和可访问的预测工具,适用于各种医疗保健环境.
- 通过早期识别PE风险,改善母亲和胎儿的结果.
主要方法:
- 在墨西哥城进行了一项前性队列研究,涉及3050名孕妇.
- 使用母体特征和血压中位数 (MoM) 的倍数,子宫动脉脉动指数和胎盘生长因子.
- 使用培训,验证和测试数据集开发并验证了弹性网ML模型,其性能由AUC和DR评估在10%的FPR.
主要成果:
- 该ML模型在预测早产期PE (pPE) (AUC=0.897) 和早期发作的PE (ePE) (AUC=0.963) 中表现出高准确度.
- 在10%的假阳性率下,检测率为PPE的76.5%,EPE的88.2%.
- 该模型实现了0.778的AUC,用于预测任何类型的PE (全PE),DR为50.1%,FPR为10%.
结论:
- 开发的ML模型显示在第一季度预测pPE和ePE时的高准确性.
- 该模型使用易于获得的母体特征和局部衍生的MoM值.
- 这种ML工具有可能促进及时干预,从而改善孕产妇和胎儿的健康.
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