慢性压力暴露的黑人和白人的差异来预测早产:可解释的,种族/种族特定的机器学习模型
Sangmi Kim1, Melinda K Higgins1, Patricia Brennan2
1Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA.
Studies in health technology and informatics
|July 25, 2024
概括
机器学习模型准确预测早产 (PTB) 风险. 模型确定了关键因素,包括低教育和暴力等慢性压力因素,特别是非西班牙裔黑人女性,从而实现了针对种族的具体干预.
科学领域:
- 生殖健康 生殖健康
- 医疗保健中的机器学习
- 健康差异 在健康上的差异
背景情况:
- 过早分娩 (PTB) 仍然是一个重要的公共卫生问题,具有复杂的促成因素.
- 现有的预测模型可能无法完全捕捉不同种族/种族群体中PTB风险的细微差别.
- 确定特定的慢性压力因素对于有针对性的PTB预防策略至关重要.
研究的目的:
- 开发精确的机器学习模型来预测早产 (PTB).
- 确定PTB的关键预测因素,区分非西班牙裔 (N-H) 黑人和N-H白人女性.
- 确定慢性压力因素对这些人群中PTB风险的影响.
主要方法:
- 开发了多变量自适应回归支柱 (MARS) 机器学习模型.
- 模型使用了来自怀孕风险评估监测系统 (2012-2017) 的数据.
- 用5倍交叉验证和曲线下面面积 (AUC) 评估模型性能.
主要成果:
- 火星模型显示PTB的高预测精度 (AUC:0.754-0.765).
- 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 (例如,母亲教育水平低,暴力) 显著影响了PTB预测,特别是对N-H黑人女性.
结论:
- 可解释,种族/种族特定的MARS模型准确地预测PTB.
- 这些模型阐明了生活压力因素对PTB风险的影响的大小.
- 研究结果支持开发针对性干预措施,以解决用于预防PTB的健康社会决定因素.
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