预测模型用于早期识别超重和肥胖儿童 - 一项国家研究
Irit Lior Sadaka1, Itamar Grotto2, Yair Sadaka3
1Department of Health Policy and Management, School of Public Health, Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er-Sheva 84105, Israel.
Nutrients
|February 13, 2026
概括
新的机器学习模型使用婴儿生长参数准确预测儿童超重风险. 与当前世界卫生组织 (WHO) 的增长图表相比,这些模型提供了改进的早期检测.
科学领域:
- 儿科内分泌学 儿科内分泌学
- 机器学习在医疗保健中的应用
- 公共卫生和预防医学.
背景情况:
- 早期的干预对于预防儿童肥胖至关重要.
- 需要准确的婴儿查模型来识别高风险个体.
- 目前世界卫生组织 (WHO) 的增长图表在预测未来超重状态方面存在局限性.
研究的目的:
- 开发和验证用于预测儿童超重的机器学习模型.
- 仅使用婴儿生长参数进行风险预测.
- 为了超越现有的世卫组织增长图的预测.
主要方法:
- 在以色列出生的婴儿 (2014-2016) 的回顾性全国队列研究.
- 为0-3,3-6和6-12个月的年龄组开发三种机器学习模型.
- 模型性能与使用曲线下的面积 (AUC) 的世卫组织增长图表预测的比较.
主要成果:
- 模型显示了高预测性能:AUC为0.76 (0-3m),0.822 (3-6m) 和0.872 (6-12m).
- 对0-3个月和3-6个月开发的模型显示,与世卫组织图表相比,早期儿童超重的预测优越.
- 一个庞大的队列 (198,503名儿童) 确保了强大的模型验证.
结论:
- 基于增长参数的机器学习模型可以更好地预测儿童超重风险.
- 这些模型可以在全球范围内在收集婴儿成长数据的系统中实现.
- 网上计算器可用于实际的风险评估.
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