在中国的Omicron波期间,COVID-19患者的肺部参与率:来自单个中心的SHAP解释机器学习研究
1Department of Radiology, Suzhou Hospital of Integrated Traditional Chinese and Western Medicine, Suzhou, China.
Frontiers in public health
|February 2, 2026
概括
在Omicron COVID-19激增期间,老年和更高的每日病例数量与增加的肺部参与有关. 这些因素是了解广泛爆发期间肺部影响的关键.
科学领域:
- 肺部病理学 肺部病理学
- 流行病学 流行病学
- 放射学 放射学是一门学科.
背景情况:
- 中国的COVID-19 Omicron在封锁后的激增导致了医疗保健系统的紧张.
- 了解肺部感染率对于疫情期间的患者护理至关重要.
- 奥米克朗变种的高传染性助长了感染的快速传播.
研究的目的:
- 评估在Omicron爆发期间肺部感染的百分比.
- 确定与肺部感染相关的主要人口和流行病因素.
主要方法:
- 基于人工智能的软件量化了每日CT扫描量作为流行病强度指标.
- GEE物流回归分析了肺部感染与年龄,性别,每日病例数量之间的关联.
- 机器学习模型和SHapley添加式解释 (SHAP) 用于解释.
主要成果:
- 年龄与肺部感染有很强的关联 (OR 1.0813,p < 0.0001).
- 每日病例数显示出一个小但显著的独立关联 (OR 1.0033,p < 0.0001).
- 性别的关联是最小的 (OR 0.8098,p = 0.0053);在ML模型中,年龄是主要因素.
结论:
- 较大的年龄和较高的每日病例数与Omicron激增期间肺部参与度增加相关.
- 人口和流行病因素对于在大型疫情中对肺部发现的特征具有重要意义.
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