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Development and validation of an interpretable machine learning model for predicting systemic inflammatory response syndrome after percutaneous nephrolithotomy: A multicenter study
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DOI：10.1016/j.ijmedinf.2026.106553.png)
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Abstract
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• Machine learning model predicts SIRS after percutaneous nephrolithotomy. • Multicenter cohort used for model development and external validation. • Random forest showed stable discrimination and calibration. • SHAP analysis improved model interpretability. • Predictors were routine perioperative clinical variables.
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