通过使用无偏见的机器学习方法减少不平等,以确定患有可预防新生儿死亡风险最高的出生
Antonio P Ramos1,2, Fabio Caldieraro3, Marcus L Nascimento4,5
1California Population Center, University of California, Los Angeles, USA. antonio.ramos@fundacaojles.org.br.
Population health metrics
|October 28, 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. 这种方法有助于政策制定者解决健康不平等问题,而不会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
- 新生儿死亡率 新生儿死亡率
背景情况:
- 新生儿死亡率存在持续差异,其中很大一部分死亡是可以预防的.
- 现有的方法在识别高风险分娩时缺乏准确性,以有效地针对政策.
- 需要先进的分析方法来精确确定易受干预的脆弱人群.
研究的目的:
- 开发和验证无偏见的机器学习 (ML) 模型,以识别患有可预防新生儿死亡高风险的出生.
- 在公共卫生干预的背景下创建一个以政策为导向的指标来评估ML模型的性能.
- 确保ML驱动的风险预测不会引入或加剧对弱势群体的偏见.
主要方法:
- 利用巴西 (2015-2017年) 的行政出生和死亡记录,涵盖近880万个出生.
- 根据政策可调整性,将新生儿死亡归类为可预防 (42,290) 和不可预防 (17,325).
- 训练和评估了六个ML算法,包括XGBoost,使用一种新的以政策为导向的指标,并评估了人口偏差.
主要成果:
- XGBoost模型表现出卓越的表现,在确定的高风险出生中,前5%的高风险出生占可预防新生儿死亡的85%以上.
- 风险预测显示,在弱势群体 (按种族,教育程度,婚姻状况,产妇年龄定义) 中,可预防死亡的比例没有统计学上显著的差异.
- 这些发现在各种风险值水平中保持一致.
结论:
- 公开可用的行政数据和ML方法可以准确地识别高风险的可预防死亡的出生.
- 开发的方法使决策者能够有效和公平地针对卫生干预措施,减少新生儿死亡率和健康差异.
- 该ML框架可适应在其他发展中国家实施,以改善新生儿健康结果.
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