计算机断层扫描的预后意义 机器学习的严重性评分 预测COVID-19患者的重症监护病房入院情况
Seyed Salman Zakariaee1, Ali Sohrabnejad2, Mostafa Shanbehzadeh3
1Department of Medical Physics, Faculty of Paramedical Sciences, Ilam University of Medical Sciences, Ilam, Iran.
Thoracic research and practice
|January 15, 2026
概括
计算机断层扫描-严重性得分 (CT-SS) 是机器学习模型预测2019年新冠肺炎 (COVID-19) 患者重症监护室 (ICU) 入院的关键预测指标. 纳入CT-SS可以提高模型准确性,用于识别关键病例并优化资源配置.
科学领域:
- 医学成像和诊断 医学成像和诊断
- 医疗保健中的人工智能
- 传染病流行病学 传染病流行病学
背景情况:
- 计算机断层扫描-严重性得分 (CT-SS) 量化了COVID-19患者的肺部参与,与疾病严重程度,ICU入院和死亡率相关联.
- 有限的数据存在于机器学习 (ML) 模型中的CT-SS的预后价值,用于预测COVID-19中的ICU入院.
研究的目的:
- 评估CT-SS在ML模型中的预后意义,用于预测COVID-19患者的ICU入院情况.
- 为了比较ML模型的性能,并没有CT-SS数据用于ICU入院预测.
主要方法:
- 来自医院数据库的815名COVID-19患者的回顾性分析.
- 通过使用CT-SS和无CT-SS的患者数据训练了七种ML方法,以评估预测性能.
主要成果:
- CT-SS被确定为ICU入院的最强预测因素.
- 将CT-SS集成到ML模型中显著提高了它们的性能.
- 结合CT-SS的k-最近邻近模型实现了93.3%的准确性和98.8%的AUC.
结论:
- 在COVID-19患者中,CT-SS是基于ML的ICU入院风险分层的关键预测因素.
- 使用CT-SS的ML模型是识别高风险COVID-19患者的有效工具.
- 这些模型可以帮助最佳地分配关键医院资源.
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