Machine learning-driven risk assessment of severe

Objective: To construct and validate an interpretable machine learning model for early prediction of severe Methods: Clinical and immunological data of 402 pediatric MPP patients were randomly divided into a training set (70% for model tuning) and a held-out internal test set (30% for final evaluation). We systematically evaluated 113 algorithms based on data collected within 24 h of admission. Results: The Gradient Boosting Machine (GBM) demonstrated optimal performance, achieving an AUC of 0.805 (95% CI: 0.724-0.886) on the held-out internal test set. The model identified 16 core predictors: dyspnea, C-reactive protein (CRP), total T lymphocytes (CD3+), sputum plug formation (PB), CD3+CD4+CD8- T cells, CD3+CD56+NKT cells, LDH, IL-6, creatine kinase (CK), PCT, ALT, IgA, abnormal coagulation function (D-dimer), CK-MB, erythrocyte sedimentation rate (ESR), and MP-DNA load. SHapley Additive exPlanations (SHAP) analysis revealed that dyspnea, elevated CRP, and decreased T lymphocytes synergistically drive severe illness risk. Conclusion: The GBM-based model effectively predicts SMPP risk. Pending prospective multi-center validation, we plan to embed this transparent, data-driven tool into electronic medical record (EMR) systems to provide real-time early warning and individualized risk assessment.