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使用机器学习预测儿童患者的COVID-19严重程度:对算法和组合方法的比较分析

Babak Pourakbari^1,2, Setareh Mamishi^1,2, Sepideh Keshavarz Valian³

¹Pediatric Infectious Disease Research Center, Tehran University of Medical Sciences, Tehran, Iran.

Scientific reports

|August 8, 2025

概括

机器学习准确地预测了儿童COVID-19的严重程度. 随机森林和整体模型确定了氧和等关键预测因素,有助于早期风险分层,以便做出更好的临床决策.

科学领域:

儿童传染病儿童传染病
医疗信息学医疗信息学
计算生物学计算生物学

背景情况:

COVID-19在儿科患者群体中提出了独特的挑战,与成人呈现不同.
现有的研究主要集中在成人COVID-19上,在儿科特定的预测模型中留下了一个空白.
机器学习 (ML) 提供了分析复杂儿科健康数据的潜力,以预测疾病严重程度.

研究的目的:

评估各种机器学习算法的有效性,以预测儿童患者的COVID-19严重程度.
确定关键的临床和实验室变量,这些变量是COVID-19儿童严重结果的重要预测因素.
评估集合ML方法提供的性能提升,如SuperLearner,用于儿科COVID-19风险分层.

主要方法:

对588名确诊COVID-19的儿科患者队列的回顾性分析.
实现和比较多个机器学习模型,包括随机森林.
使用SuperLearner整体模型来汇总预测并提高准确性.
包括人口,临床和实验室数据用于模型培训和验证.

主要成果:

随机森林模型实现了高预测性能:准确率为90.1%,灵敏度为90.2%,特异性为90.1%.
超级学习者组合模型进一步增强了预测能力,产生了最低的平均风险估计.
儿童严重COVID-19的重要预测因素包括氧和度,呼吸系统参数和特定的实验室标记.

结论:

机器学习,特别是组合方法,在儿科COVID-19中显示出准确风险分层的巨大潜力.
这些预测模型可以帮助临床医生早期识别高风险儿科患者.
整合ML工具可以优化临床决策和儿童COVID-19管理的资源配置.

关键词:

在 COVID-19 疫情中,组合模型模型组合模型机器学习机器学习儿科患者儿科患者随机的森林随机的森林严重程度预测的预测.

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