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乌拉圭儿童血预测:个人与社区级合奏学习者
Seth Frndak1, Elena I Queirolo2, Nelly Mañay3
1Department of Epidemiology and Environmental Health, University at Buffalo, The State University of New York USA, Buffalo, New York, United States of America.
个人层面的因素,比如家庭用水中的,最能预测儿童血液中的含量 (BLLs). 机器学习模型表明,专注于这些近距离预测因素是城市儿童准确暴露预测的关键.
科学领域:
- 环境健康 环境健康
- 流行病学 流行病学
- 机器学习在公共卫生中的应用
背景情况:
- 儿童血液中含量 (BLLs) 构成重大健康风险,但准确的预测仍然具有挑战性.
- 了解个人与邻里因素在预测BLL中的相对重要性对于有针对性的干预至关重要.
研究的目的:
- 实施整体机器学习方法,以确定城市儿童中BLL升高 (≥2μg/dL) 的关键预测因素.
- 为了比较模型的预测性能,使用个人级别,社区级别和组合预测器.
主要方法:
- 乌拉圭蒙特维多603名儿童 (平均年龄~7岁) 的横截面研究 (2009-2019).
- 利用77个个体变量和32个社区变量来预测BLL,使用三种整体机器学习模型:Ensemble-I (个体),Ensemble-N (社区) 和Ensemble-All (组合).
- 用ROC AUC,精度,灵敏度和特异性来评估模型性能.
主要成果:
- 个人级别模型 (Ensemble-I) 的表现最好,AUC为0.75,与组合模型 (Ensemble-All) 相似.
- 社区级模型 (Ensemble-N) 的表现严重低 (AUC:0.51).
- 在表现最好的模型中,入学年和家庭用水率是最重要的预测因素. 一些邻里变量,如巴士路线密度,在组合模型中也很重要.
结论:
- 个人层面的因素,特别是家庭特征,比邻居层面的因素更能预测童年BLL.
- 未来的暴露预测模型应该优先考虑近接的,个人级预测器,以提高准确性.
- 虽然个人因素占主导地位,但结合选择的邻里数据可以提高预测精度.
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