ArticleBiosafety and health2025
Random forest-based predictor selection and pneumonia risk probability assessment in acute respiratory infections: A cross-sectional study in Chongqing, China, 2023-2024.
Article in Biosafety and 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.
- Dermatomyositis does not adversely affect functional outcomes after total hip arthroplasty: a 1:2 matched case-control study.Arthroplasty (London, England) · 2026Article
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9 authors.
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Abstract
Progression of acute respiratory infection (ARI) to pneumonia increases severity and healthcare burden. Limited evidence exists on using machine learning to identify predictors from demographics, clinical, and pathogen detection data. This study aimed to identify pneumonia predictors in ARI patients using machine learning methods. This observational study was conducted in Chongqing, China, from September 2023 to April 2024. Outpatients and inpatients with ARI were recruited weekly. A random forest algorithm was used for predictor selection, followed by a logistic regression-based nomogram to analyze the probability of pneumonia. Among the 1,638 patients with ARI, those with pneumonia had higher rates of influenza A virus (IFV-A) (49.2 % vs. 39.6 %), influenza B virus (26.3 % vs. 18.6 %), and respiratory syncytial virus (6.1 % vs. 1.9 %) infection than those without pneumonia. In the subgroup of 79 patients with comprehensive blood tests, pneumonia was positively associated with hemoglobin (130.00 g/L vs. 124.00 g/L), blood urea nitrogen (5.73 mmol/L vs. 4.85 mmol/L), C-reactive protein (36.10 mg/L vs. 25.25 mg/L), procalcitonin (0.11 μg/L vs. 0.07 μg/L), and D-dimer (0.95 μg/L vs. 0.80 μg/L) levels, whereas pneumonia was inversely associated with neutrophils (4.20 × 10
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