Construction and interpretability evaluation of a prediction model for radiographic pneumonia in children at outpatient and emergency departments.

BACKGROUND: Radiographic confirmation is crucial for pediatric pneumonia diagnosis, but chest X-ray overuse in outpatient and emergency settings raises concerns about unnecessary radiation and resource utilization. This study aimed to develop and validate a machine learning-based prediction model to assist in chest X-ray decision-making for children with suspected respiratory tract infection. METHODS: A retrospective cohort of 4,822 children with respiratory infections from Xuchang Central Hospital (Oct 2023-Jun 2024) was analyzed. Four feature selection methods identified key predictors, which were used to train an XGBoost model. External validation used an independent cohort. Model performance, interpretability, and clinical utility were assessed via ROC curves, SHAP analysis, and decision curve analysis. RESULTS: Four predictors were selected: hs-CRP, age, neutrophil count, and lymphocyte count. The model achieved an AUC of 0.773 in the test set (sensitivity 64.8%, specificity 74.6%, NPV 90.2%) and 0.751 in external validation. SHAP analysis highlighted hs-CRP and age 8-12 years as key contributors. Decision curve analysis showed superior net benefit at thresholds 0%-40%. A risk cutoff of 0.20 identified 56.5% of patients as low-risk. CONCLUSION: his XGBoost model, incorporating four objective variables, demonstrates good predictive performance for radiographic pneumonia in pediatric outpatient and emergency settings. Its high negative predictive value supports its use as a screening tool to reliably exclude pneumonia and reduce unnecessary chest X‑rays. The model has been deployed as a web-based tool for clinical use.