Development and validation of a machine learning-driven framework for differentiating pediatric bronchopneumonia from lobar pneumonia: a multicenter investigation

The Gradient Boosting model, as delineated in this study, markedly advances the differential diagnosis of pediatric bronchopneumonia and lobar pneumonia, delivering high precision and resilience. It serves as an efficacious and dependable clinical decision-support instrument. By incorporating pivotal biomarkers like P-LCT and LYM%, this model illuminates pathophysiological traits, enhances antibiotic stewardship, and guides hospitalization choices, thereby diminishing healthcare resource wastage and ameliorating patient outcomes. These insights furnish vital backing for precision medicine and acute care management.