更好的个体级风险模型可以提高早期死亡干预的向性和拯救生命的潜力
Chad Hazlett1,2, Antonio P Ramos3,4, Stephen Smith2
1Professor, Department of Political Science and Department of Statistics and Data Science, University of California Los Angeles, Los Angeles, USA.
Scientific reports
|December 8, 2023
概括
机器学习模型可以显著改善撒哈拉以南非洲的婴儿死亡率减少. 通过使用产前数据,这些灵活的风险模型可以更准确地识别高风险婴儿,而不仅仅是基于财富的准.
科学领域:
- 公共卫生 公共卫生
- 人口统计学 人口统计学
- 机器学习应用 机器学习应用
背景情况:
- 婴儿死亡率仍然很高,在撒哈拉以南非洲地区分布不均.
- 有效的干预措施受限于精确地针对死亡风险最高的婴儿的能力.
- 目前的定位策略通常依赖于简单的财富或收入模型,限制了它们的有效性.
研究的目的:
- 评估灵活机器学习 (ML) 风险模型的有效性,以识别高风险婴儿.
- 将ML模型的性能与传统的基于财富的准方法进行比较,以减少婴儿死亡率.
- 展示如何改进目标化可以提高现有干预措施的救命潜力.
主要方法:
- 利用机器学习 (ML) 风险模型,将22个撒哈拉以南非洲国家的人口和健康调查 (DHS) 的25个出生前变量纳入其中.
- 开发了风险评分,以确定人口中风险最高的10%.
- 将ML模型的预测准确度与仅基于财富数据的模型进行比较.
主要成果:
- 机器学习模型成功地确定了人口中风险最高的10%,占婴儿死亡率的15-30%.
- 关键的预测变量包括财富之外的因素.
- 仅使用财富数据的模型只比随机机会表现得稍好一些.
结论:
- 灵活的ML风险模型比针对婴儿死亡率干预的传统方法提供了实质性的改进.
- 通过先进的建模准确识别高风险分娩,可以显著提高拯救生命的疗法的影响.
- 国土安全部数据的广泛可用性支持这些先进的准策略的可扩展实施.
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