减少不平等 采用无偏的机器学习方法来识别具有可预防新生儿死亡风险最高的出生
medRxiv : the preprint server for health sciences
|October 3, 2025
概括
机器学习准确地识别出高风险的出生,以进行有针对性的干预,减少可预防的新生儿死亡和健康不平等,而不对弱势群体产生偏见.
科学领域:
- 公共卫生 公共卫生
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 新生儿死亡率在某些人群中仍然很高,尽管总体下降.
- 许多新生儿死亡是可以预防的,需要精确的风险识别方法.
- 现有的方法缺乏准确性,无法有效地针对高风险分娩进行干预.
研究的目的:
- 开发无偏见的机器学习 (ML) 方法,准确识别高风险的新生儿死亡.
- 为决策者提供工具,有效地针对健康干预措施,减少新生儿死亡率和健康不平等.
主要方法:
- 利用了巴西 (2015-2017) 的行政出生和死亡记录,覆盖了近880万个出生.
- 训练和评估六个ML算法,以预测可预防的新生儿死亡.
- 开发了一种以政策为导向的新型指标,并评估了弱势群体公平性模型的表现.
主要成果:
- XGBoost表现出卓越的表现,预测高风险分娩的前5%占可预防新生儿死亡的85%以上.
- 风险预测显示,没有基于种族,教育,婚姻状况或母亲年龄的不利人群的统计偏见.
- 这些发现在不同的风险值水平上都是正确的.
结论:
- 公开可用的数据和ML方法可以准确地识别高风险的可预防死亡的出生.
- 开发的方法使得有针对性,有效和公正的健康干预成为可能.
- 这一战略可以指导政策制定者减少新生儿死亡率和健康差异,在其他发展中国家也有可能适用.
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