Development and validation of an explainable model for Mycoplasma pneumoniae infection in children.

This study aimed to develop an explainable early diagnostic model for Mycoplasma pneumoniae (MP) infection in children and assess clinicians' perceptions before and after its deployment. Data were collected from children with acute fever or cough at Linyi Maternal and Child Healthcare Hospital and Shanghai Children's Medical Center in 2023. Serology-defined MP infection was determined based on a fourfold or greater change in MP-IgM or MP-IgG titers between the acute and convalescent phases. Patients were split into training and test sets at a 7:3 ratio, and temporal validation was performed using data collected in 2024. PCR testing was not systematically available in this cohort and was therefore not used as a comparator or reference standard. Recursive Feature Elimination (RFE) optimized feature selection. Seven models were developed; the best was chosen based on AUC value and interpreted using SHAP. Models were deployed via Streamlit, and clinician feedback was surveyed before and after three months of use. The optimal model, using 11 features, achieved AUCs of 0.89 in the internal test set and 0.84 in the temporal validation cohort for identifying serology-defined MP infection, with TNFβ and IL-2 emerging as high-impact features. Separate models for under and over 60-month-old children also performed well. Clinicians' trust in the model significantly increased after three months (P < 0.001) and was correlated with usage frequency (P < 0.01). We developed explainable models to support the early identification of serology-defined MP infection in children. TNFβ and IL-2 may represent informative immune-related features, although further PCR-based validation and mechanistic studies are needed. Clinician trust and usage increased after model deployment.