Random forest-based predictor selection and pneumonia risk probability assessment in acute respiratory infections: A cross-sectional study in Chongqing, China, 2023–2024
Abstract
Highlights • Scientific questions: Progression of acute respiratory infection (ARI) to a severe lower respiratory tract infection, such as pneumonia, can increase clinical severity and place a greater burden on healthcare resources. Currently, limited evidence exists on the use of machine learning to identify key pneumonia predictors from a broad range of potential factors, including demographic, clinical, and pathogen detection data.• Evidence before this study: Certain respiratory pathogens and clinical indicators have been identified as potential risk factors for pneumonia. However, the strength of the association between each indicator and pneumonia development remains unclear, and there is a need to identify appropriate predictors using comprehensive data analysis methods.• New findings: We used a random forest algorithm and a logistic regression-based nomogram to identify significant predictors of pneumonia among ARI patients. Elevated D-dimer levels and influenza A virus infection were found to be significantly associated with an increased risk of pneumonia. Other important indicators identified included age, neutrophils, lymphocytes, total protein, globulin, triglycerides, total bilirubin, and procalcitonin.• Significance of the study: The findings provide a basis for identifying high-risk ARI patients who may progress to pneumonia. The identified predictors can be used to develop clinical prediction models to guide early diagnosis and intervention, potentially…
How strong is the evidence?
Evidence Hub reads this as a cross-sectional, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause.
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