Table 1_Interpretable machine learning models for predicting New-onset acute organ injury within 48 hours of admission in patients with community-acquired acute cholangitis.xlsx

In the training set, univariable logistic regression (LR) was used for screening, followed by LR with least absolute shrinkage and selection operator (LASSO) penalty via 5-fold cross-validation. Five models were developed: random forest (RF), support vector machine (SVM), LR, neural network (NN), and adaptive boosting (AdaBoost). Optimization used 5-fold stratified cross-validation and grid search.

The optimal model was selected by cross-validated area under the receiver operating characteristic curve (ROC-AUC) and evaluated for discrimination, calibration, and net clinical benefit. SHapley Additive exPlanations (SHAP) was used for interpretation.</p>Results<p>RF and SVM achieved the highest mean cross-validated ROC-AUC (0.847 each). RF was selected per the prespecified tie-breaking protocol.

In the independent test set, RF achieved an ROC-AUC of 0.820 [95% confidence interval (CI): 0.720–0.906], accuracy of 72.8%, sensitivity of 80.6%, specificity of 68.9%, positive predictive value (PPV) of 56.8%, negative predictive value (NPV) of 87.5%, and F1-score of 0.667. The Brier score was 0.156, calibration intercept 0.014, and calibration slope 1.252. Decision curve analysis (DCA) demonstrated a positive net clinical benefit across threshold probabilities ranging from 0.01 to 0.80.

The eight key RF predictors were C-reactive protein (CRP), aspartate aminotransferase-to-platelet ratio index (APRI), MAP, prognostic nutritional index (PNI), gamma-glutamyl transferase-to-albumin ratio (GAR), age, neutrophil-to-lymphocyte ratio (NLR), and drink history.</p>Conclusion<p>The RF model predicted new-onset AOI within 48 h in patients with CAAC and provided interpretable individual risk information through SHAP analysis.</p>