预测算法在估计印度,马里和尼日利亚零剂量儿童的风险方面的表现
Arpita Biswas1, John Tucker2, Sebastian Bauhoff3
1Center for Research on Computation and Society, Harvard University John A Paulson School of Engineering and Applied Sciences, Cambridge, Massachusetts, USA.
BMJ global health
|October 11, 2023
概括
预测算法可以有效地识别错过常规疫苗接种 (零剂量) 的儿童. 这种方法提供了一种低成本,高效的方法来针对干预措施并改善全球疫苗接种公平.
科学领域:
- 全球健康 全球健康
- 公共卫生 公共卫生
- 流行病学 流行病学
背景情况:
- 低收入和中等收入国家的许多儿童错过了常规疫苗接种,称为"零剂量" (ZD).
- 缺乏有效和高效的策略来识别和准ZD儿童.
研究的目的:
- 用例行行政数据评估预测算法的有效性,以识别ZD儿童.
- 将算法定位与传统的地理方法进行比较.
主要方法:
- 监督学习算法应用于印度,马里和尼日利亚的行政数据.
- 预测变量包括卫生系统联系和地理数据.
- 用特异性,敏感性和F1评分来评估性能,并进行特征重要性分析.
主要成果:
- 具有成本敏感性的Ridge分类在识别ZD儿童方面表现出高的特异性.
- 除了最初的七个预测因素之外,增加预测因素并没有显著改善性能.
- 算法定位显示,排斥误差比地理定位低9.1%,并且在更多地区识别了ZD儿童.
结论:
- 预测算法可以有效和负担得起地识别ZD儿童.
- 这种方法可以指导干预措施,以减少ZD的流行率和改善疫苗接种公平.
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