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使用基于机器学习的方法预测孕前的预测模型:中国的一项基于人口的队列研究

Taishun Li^1,2, Mingyang Xu³, Yuan Wang¹

¹Department of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.

Frontiers in endocrinology

|June 26, 2024

概括

机器学习模型使用早期怀孕标志物准确预测子宫前. 投票分类器在早产前方面表现优异,有助于早期干预,改善母亲和胎儿的结果.

科学领域:

产科和妇科产科和妇科
医疗信息学医疗信息学
孕产妇和胎儿医学孕产妇和胎儿医学

背景情况:

孕前是孕产妇和围产期发病率的重要原因,其病因不明.
早期识别和干预,如阿司匹林治疗,对于预防孕前至关重要.
开发有效的预测模型对于早期查和风险分层至关重要.

研究的目的:

开发一个强大的基于机器学习的预测模型来预测孕前.
通过早期妊娠标志物来识别子宫前的高风险怀孕.
为早期查和预测子宫前提供一个有效的工具.

主要方法:

一项前性队列研究包括5116名孕妇.
在怀孕11-13+6周时收集了母亲的特征,生物物理和生物化学标记 (MAP,UtPI,PAPP-A,PLGF).
应用了五种机器学习算法 (逻辑回归,额外树,投票,高斯过程,堆叠),并使用交叉验证进行了验证.

主要成果:

投票分类器在预测早产前方面表现优异 (AUC=0.884,DR=0.625在10%的FPR).
在预测整体孕产前方面,PLGF的贡献高于PAPP-A,而早产前则呈相反的趋势.
机器学习模型的性能与胎儿医学基金会的竞争风险模型相当.

结论:

开发的机器学习模型为大规模的孕前查提供了一个可访问的工具.
早期预测可以帮助减少产前的疾病负担.
通过基于准确预测的及时干预,预计改善母亲和胎儿的结果.

关键词:

队列队列队列队列具有竞争力的风险模型.机器学习是机器学习.孕前症是什么意思投票分类器的投票分类器

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