Development and validation of an interpretable machine learning model using routine laboratory biomarkers to stratify severe pneumonia risk in young children

This interpretable CatBoost model accurately stratifies pediatric severe pneumonia risk using routine laboratory data. Clinical implementation via the web tool may facilitate early intervention in resource-limited settings, though extensive external validation is warranted.