预测COVID-19患者的严重呼吸衰竭:一种机器学习方法
Bahadır Ceylan1, Oktay Olmuşçelik2, Banu Karaalioğlu3
1Department of Infectious Diseases and Clinical Microbiology, Medical Faculty, Istanbul Medipol University, Istanbul 34214, Türkyie.
Journal of clinical medicine
|December 17, 2024
概括
机器学习可以准确地预测COVID-19患者的严重呼吸衰竭. 关键预测因素包括低淋巴细胞计数和肺成像得分,有助于早期干预以获得更好的结果.
科学领域:
- 医学信息学 医学信息学
- 肺部病理学 肺部病理学
- 医疗保健中的机器学习
背景情况:
- 预测COVID-19严重呼吸衰竭的机器学习模型显示,由于不同的变量选择,结果各不相同.
- 严重的呼吸衰竭被定义为需要高流量氧气,持续的正气道压力或机械通风.
研究的目的:
- 使用机器学习预测COVID-19患者严重呼吸衰竭的发展.
- 确定预测COVID-19患者严重呼吸衰竭的最关键变量.
主要方法:
- 在320名COVID-19患者中进行了一项回顾性,横截面的研究,这些患者患有轻度呼吸衰竭.
- 使用XGBoost,支持向量机,多层感知子,k-最近邻居,随机森林,决策树,后勤回归和天真贝叶斯.
- 评估的预测准确性和接受器运行特征曲线下的区域 (ROC-AUC).
主要成果:
- XGBoost,支持向量机,k-最近邻居,物流回归和多层感知子显示出高精度 (0.7187-0.75).
- 后勤回归实现了最高的ROC-AUC值 (0.7274).
- 确定了关键预测因素:低淋巴细胞数量,高计算机断层扫描肺部得分 (上部区域),高中性粒细胞数量,降低CRP水平,高查尔森并发症指数和高甲.
结论:
- 机器学习方法,特别是后勤回归,可以成功预测COVID-19的严重呼吸衰竭.
- 在CT扫描中,淋巴细胞数量和上肺部区域的参与是严重呼吸衰竭的最强有力的预测因素.
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