使用机器学习来对抗儿童急性营养不良,并预测在门诊治疗期间的体重增加,使用简化组合协议
Luis Javier Sánchez-Martínez1, Pilar Charle-Cuéllar2, Abdoul Aziz Gado3
1Unit of Physical Anthropology, Department of Biodiversity, Ecology and Evolution, Faculty of Biological Sciences, Complutense University of Madrid, 28040 Madrid, Spain.
Nutrients
|December 17, 2024
概括
机器学习使用社会经济数据准确地预测急性营养不良儿童的低体重增加. 这有助于在人道主义环境中定制治疗方案,以获得更好的结果.
科学领域:
- 公共卫生 公共卫生
- 机器学习 机器学习
- 儿科 儿科 儿科
背景情况:
- 儿童急性营养不良影响全球4500万儿童.
- 世界卫生组织建议每周对治疗的体重进行监测.
- 使用手臂周长的简化协议在紧急情况下使用.
研究的目的:
- 使用机器学习预测急性营养不良儿童的体重增加.
- 确定影响体重增加的社会经济因素.
- 在人道主义环境中优化治疗方案.
主要方法:
- 在51个社会经济变量上利用机器学习 (随机森林,整体模型).
- 使用变量选择使用随机森林 (VSURF) 选择的关键变量.
- 使用的接收器运行特征 (ROC) 曲线来确定最佳切断点.
主要成果:
- 社会经济因素如水,卫生,护理人员就业和治疗机会至关重要.
- 集成模型实现了R2 = 0.55,超过了单个算法.
- 确定了AUC 0.777的最佳切线 (<6.5g/kg/day),在识别低体重增加方面取得了84%的成功.
结论:
- 对体重增加而言,特定的背景界限是必不可少的.
- 机器学习技术在人道主义环境中为优化营养不良治疗提供了实际实用性.
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