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Development and external validation of an interpretable machine learning model for early prediction of stroke-associated pneumonia: a multicenter study
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DOI：10.1016/j.ijmedinf.2026.106543.png)
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Abstract
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• An interpretable machine learning model was developed to predict 7-day stroke-associated pneumonia. • The model used 10 routinely available predictors collected within 24 h of admission. • Stochastic gradient boosting showed the best overall performance across internal validation metrics. • External validation confirmed good discrimination, calibration, and clinical utility of the model. • A Streamlit-based online calculator was deployed for individualized and batch risk estimation.
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