Explainable machine learning for the early differentiation of pediatric bronchopneumonia using routine laboratory parameters.
Support Vector Machine model distinguished pediatric bronchopneumonia from upper respiratory infection with high accuracy
Explainable machine learning for the early differentiation of pediatric bronchopneumonia using routine laboratory parameters.
Therefore, this study aimed to develop and internally evaluate an early triage model to distinguish pediatric bronchopneumonia (bronchopneumonia) from uncomplicated upper respiratory tract infection using routine, cost-effective laboratory parameters analyzed with machine-learning algorithms.
This retrospective study consecutively enrolled 532 pediatric patients who presented with mild respiratory symptoms at their initial visit, comprising 218 in the bronchopneumonia group and 314 in the Upper Respiratory Tract Infection (Upper Respiratory Tract Infection) group.
AUROC 0.921, 1.0 is perfect discrimination between bronchopneumonia and Upper Respiratory Tract Infection
Based on Platt-scaled probability estimates, the Support Vector Machine model showed the lowest Brier score among the evaluated models, with a Brier score of 0.112.
Decision curve analysis confirmed this model's positive net clinical benefit across a broad range of threshold probabilities.
SHAP analysis further elucidated the nonlinear contribution weights of multiple conventional parameters at specific physiological thresholds.
The multidimensional Support Vector Machine risk quantification model, based on nine routine laboratory parameters, provides an accurate and objective assessment of pediatric bronchopneumonia risk.
This model holds significant potential for clinical translation as a noninvasive, cost-effective triage tool in emergency departments. Its application could effectively reduce unnecessary radiographic screening and excessive antibiotic use.