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利用机器学习来预测儿童COVID-19患者的入院情况 (PrepCOVID-Machine)

Chuin-Hen Liew¹, Song-Quan Ong², David Chun-Ern Ng³

¹Hospital Tuanku Ampuan Najihah, Jalan Melang, 72000, Kuala Pilah, Negeri Sembilan, Malaysia.

Scientific reports

|January 24, 2025

概括

机器学习模型现在可以预测儿童的COVID-19入院情况. 适应性增强模型准确地识别了需要住院治疗的儿科患者,帮助临床决策.

科学领域:

儿童传染病儿童传染病
医疗信息学医疗信息学
医疗保健中的机器学习

背景情况:

随着COVID-19的流行,全球医疗保健资源受到严重压力.
准确预测儿科COVID-19住院的情况对于有效的资源配置至关重要.
预测亚洲儿童COVID-19住院治疗的现有机器学习模型是有限的.

研究的目的:

开发和验证机器学习模型,用于预测0-12岁儿童的COVID-19住院治疗.
确定与儿科COVID-19住院相关的关键临床变量.
为临床医生提供一个工具,以帮助儿童COVID-19患者的医疗处置决策.

主要方法:

利用了1988名马来西亚儿童 (0-12岁) 的二次数据,这些儿童在2020年2月至2022年3月期间被诊断出患有COVID-19的.
用于特征选择的递归特征消除 (RFE),确定12个显著变量.
通过网格搜索训练和优化了7个监督机器学习分类器,包括适应性提升.

主要成果:

递归特征消除确定了住院治疗的12个关键预测因素:年龄,男性性别,发烧,咳,鼻,呼吸短促,吐,腹,,体温,胸部深入,以及异常的呼吸声音.
在外部验证过程中,自适应增强分类器表现出最高的性能,实现了0.95.9的接收器操作特征曲线 (AUROC) 下面面积.
开发的模型有效地预测了儿科患者COVID-19住院的可能性.

结论:

经过验证的自适应增强模型是预测儿童COVID-19入院的可靠工具.
这种预测模型可以帮助前线临床医生及时准确地做出医疗处置决策.
这些发现有助于改善COVID-19大流行期间的医疗保健管理,特别是对于儿科患者.

关键词:

人工智能的人工智能是人工智能.医院的入院情况机器学习机器学习儿科COVID-19的治疗方法这就是SARS-CoV-2病毒.

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