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深度学习用于因果推断使用低出生体重在助产士领导的连续护理干预在北Shoa地区,埃塞俄比亚的持续护理干预

Wudneh Ketema Moges^1,2,3, Awoke Seyoum Tegegne¹, Aweke A Mitku^1,4

¹Department of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia.

Frontiers in artificial intelligence

|October 10, 2025

概括

助产士领导的连续护理 (MLCC) 可能会降低低出生体重 (LBW) 的风险. 因果深度学习模型准确地预测LBW并估计治疗效果,支持个性化的产前护理,以获得更健康的婴儿结果.

科学领域:

孕产妇和儿童的健康
生物统计学生物统计学
医疗保健中的人工智能

背景情况:

低出生体重 (LBW) 是婴儿健康的重要风险因素,需要及早识别高风险怀孕.
助产士领导的持续护理 (MLCC) 是旨在改善母亲和婴儿结果的干预措施.

研究的目的:

使用因果深度学习 (CDL) 建立MLCC和LBW之间的因果关系.
为了估计MLCC对LBW的异质治疗效应.
利用先进的AI来减少偏差和准确预测LBW.

主要方法:

准实验性研究设计,涉及埃塞俄比亚1166名女性 (2019年8月至2020年9月).
应用CDL模型,包括反事实卷积神经网络和贝叶斯脊回归.
使用反事实回归与瓦斯斯坦距离 (CFR-WASS) 和最大平均差异 (CFR-MMD) 来减少偏差和改进反事实估计.

主要成果:

深度神经网络 (DNN) 模型实现了81.4%的测试准确度和0.88 AUC的LBW预测.
确定了吸附综合症 (MAS) 和LBW之间的统计联系,尽管不是直接的因果关系.
因果模型 (CFR-WASS,CFR-MMD) 证明了MLCC降低LBW风险的潜力,具有准确的平均治疗效应 (ATE) 和精确估计异质效应 (PEHE) 值.

结论:

CDL模型有效地预测了LBW,并确定了MAS和医疗保健准入等促成因素.
来自CFR-WASS/CFR-MMD的准确PEHE和ATE估计支持数据驱动的产前护理.
在减少LBW方面,MLCC显示出有前途,使得针对性的干预措施能够改善母亲和婴儿健康结果.

关键词:

平均治疗效果平均治疗效果因果深度学习是一种深度学习.出生时体重较低的婴儿.助产士领导的连续护理是助产士领导的.在估计异质治疗效应时的精度.

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