慢性压力暴露的黑人和白人的差异来预测早产:可解释的,特定于种族/种族的机器学习模型
Sangmi Kim1, Patricia A Brennan2, George M Slavich3
1Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA. sangmi.kim@emory.edu.
BMC pregnancy and childbirth
|June 22, 2024
概括
慢性压力因素对早产风险的影响因种族而异. 机器学习模型确定了关键因素,突出了解决社会决定因素的必要性,以减少早产率的黑人和白人差异.
科学领域:
- 生殖健康 生殖健康
- 健康差异 在健康上的差异
- 机器学习在医学中的应用
背景情况:
- 早产的种族/民族差异与差异性的慢性压力暴露有关.
- 以前的研究还没有充分探索慢性压力因素对早产产生的累积,互动和人口特异性影响.
- 需要新的建模方法来捕捉复杂的,高维的关联,这些关联可能因种族/种族而异.
研究的目的:
- 开发和应用机器学习模型,以更准确地预测早产.
- 为了确定特定的慢性压力和风险因素驱动早产风险.
- 在非西班牙裔黑人和非西班牙裔白人怀孕人口中调查这些关联.
主要方法:
- 多变量自适应回归脊柱 (MARS) 模型被用来预测早产.
- 为非西班牙裔黑人,非西班牙裔白人和联合研究样本开发了模型.
- 用ROC曲线下的面积 (AUC) 和变量重要性分析来评估模型性能.
主要成果:
- 特定于种族/种族的MARS模型表现出高准确度 (AUC:0.754-0.765),表现与组合模型一样好或更好.
- 跨人群的关键预测因素包括产前护理访问,膜的过早破裂和医疗条件.
- 慢性压力因素,如母亲的教育水平低和亲密伴侣暴力,是早产的重要预测因素,仅限非西班牙裔黑人女性.
结论:
- 调查结果强调了针对慢性压力因素等中上游社会决定因素的重要性.
- 专注于这些因素的干预措施对于减少非西班牙裔黑人妇女过度早产风险至关重要.
- 解决慢性压力因素可以帮助缩小在美国早产率中持续存在的黑人-白人差距.
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