ArticleFrontiers in public health2025
Lung involvement percentage in patients with COVID-19 during the Omicron wave in China: a SHAP-explained machine learning study from a single center.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Development and validation of a machine learning-based model for predicting delirium risk in postoperative brain tumor patients in the Intensive Care Unit.Journal of neuro-oncology · 2026Article
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20 authors.
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Abstract
Background: Following the lifting of China's stringent lockdown policy on December 7, 2022, COVID-19 cases surged in a pattern, creating unprecedented strain on healthcare systems. The Omicron variant, characterized by high transmissibility and rapid spread, led to a sharp rise in infections. Understanding its clinical impact-particularly on lung involvement percentage-is crucial for optimizing patient care under such outbreak conditions. This study aimed to assess the extent of lung involvement percentage during the outbreak and its major associations. Methods: The hospital's daily computed tomography examination volume was quantified using artificial intelligence-based pulmonary inflammation analysis software and used as an indicator of epidemic intensity. Associations between lung involvement percentage and age, sex, and daily case counts were evaluated using GEE Logistic Regression, complemented by machine learning models. Model interpretation was performed using SHapley Additive exPlanations. Results: GEE Logistic regression demonstrated that age was strongly associated with lung involvement (OR 1.0813, 95% CI 1.0703-1.0925, Conclusion: During the Omicron surge, greater age and higher daily case counts were associated with higher lung involvement percentage. These associations highlight the relevance of demographic and epidemic factors in characterizing pulmonary findings during large-scale outbreaks.
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