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机器学习与多变量逻辑回归对抗,用于预测患有Omicron变体感染的住院儿童的严重COVID-19病例
Pan Liu1, Zixuan Xing2, Xiaokang Peng1
1Department of Infectious Diseases, Xi'an Jiaotong University Affiliated Children's Hospital, Xi'an, Shaanxi, China.
Journal of medical virology
|February 2, 2024
概括
机器学习模型准确地预测了感染Omicron变种的儿童的严重的2019年冠状病毒疾病 (COVID-19). 这些模型识别了关键的风险因素,改善了儿童COVID-19预后的传统后勤回归.
科学领域:
- 儿童传染病 儿童传染病
- 病毒性呼吸道疾病的流行病学.
- 计算生物学和生物信息学
背景情况:
- 奥米克朗变种的激增导致儿童严重的冠状病毒疾病2019 (COVID-19) 住院病例增加.
- 准确预测严重疾病对于儿童COVID-19病例的及时干预至关重要.
研究的目的:
- 在Omicron变种浪潮期间,在住院儿童中识别与严重COVID-19相关的风险因素.
- 开发和比较使用机器学习和物流回归的预后模型,用于预测儿科患者严重的COVID-19.
主要方法:
- 在Omicron变种期间,对544名COVID-19的住院儿童进行了回顾性分析.
- 使用多变量逻辑回归和机器学习 (RandomForest + TomekLinks) 来构建预测模型.
- 分析了关键的临床和实验室参数,以确定严重疾病的独立风险因素.
主要成果:
- 发烧,咳,和吐是常见的症状. 严重的COVID-19的独立风险因素包括年龄 (1-3岁),并发症,咳,中性粒细胞与淋巴细胞的比例,乳酸脱酶,血尿素和总胆红素.
- 与多变量逻辑回归 (AUC 0.7770) 相比,机器学习模型显示出更高的预测准确性 (AUC 0.8590).
- 一个使用前10个变量的随机森林模型实现了0.8210.0的AUC.
结论:
- 机器学习方法在预测感染Omicron变种的儿童严重的COVID-19结果时提供了更高的准确性.
- 识别特定的风险因素,如年龄,并发病症和实验室标记,有助于分层儿童患者的严重疾病风险较高.
- 这些发现支持将先进的计算模型集成到临床实践中,以更好地管理儿科COVID-19.
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