Development and multi-database validation of interpretable machine learning models for predicting In-Hospital mortality in pneumonia patients: A comprehensive analysis across four healthcare systems.

backgroundExisting machine learning studies for pneumonia mortality prediction are limited by small sample sizes, single-center designs, and lack of comprehensive external validation across diverse healthcare systems. No previous study has systematically validated machine learning models across multiple large-scale databases for pneumonia mortality prediction.

methodsThis retrospective multicenter study utilized four large-scale databases to develop and validate machine learning models for predicting in-hospital mortality in pneumonia patients. MIMIC-IV served as the primary training dataset (9,410 patients), with external validation on MIMIC-III (2,487 patients), eICU (13,541 patients), and an in-house multicenter prospective cohort from fudan university (345 patients). Five algorithms were implemented: Random Forest, XGBoost, Logistic Regression, LASSO, and Support Vector Machine. Feature selection used the Boruta algorithm across 21 variables. Model interpretability was assessed using SHAP analysis.

resultsThe cohort comprised 25,783 pneumonia patients with mortality rates of 17.1%-38.3% across databases. Nine consistently important features were identified: age, diastolic blood pressure, heart rate, temperature, respiratory rate, creatinine, blood urea nitrogen, platelet count, and white blood cell count. XGBoost achieved optimal performance with training AUC 0.747 (95% CI: 0.733-0.761) and robust external validation AUCs of 0.672 (MIMIC-IV testing), 0.670 (MIMIC-III), 0.695 (eICU), and 0.653 (FAHZU). SHAP analysis revealed platelet count as the most influential predictor, followed by blood urea nitrogen and age.

conclusionsThis study represents the first comprehensive multi-database validation of machine learning models for pneumonia mortality prediction, demonstrating superior performance compared to traditional scoring systems. The XGBoost model with SHAP interpretability provides a robust tool for clinical decision support, with consistent validation across four databases including our in-house prospective cohort.