Machine learning-based prediction of severe Mycoplasma pneumoniae pneumonia in pediatric patients

Mycoplasma pneumoniae pneumonia (MPP) is a leading cause of pediatric community-acquired pneumonia, with severe cases (SMPP) posing significant risks of complications and prolonged hospitalization. Early identification of SMPP remains challenging due to nonspecific clinical presentations, underscoring the need for robust predictive tools. While artificial intelligence (AI) has shown promise in medical diagnostics, its application to non-imaging clinical data, such as MPP risk stratification, is underexplored. This study leverages machine learning (ML) to bridge this gap, aiming to transform retrospective clinical data into actionable predictive insights for SMPP. By integrating multidimensional clinical variables, we address the critical unmet need for early, accurate risk assessment in pediatric MPP management.