Table 3.
Optimal machine-learning algorithms for predicting various outcomes corresponding to patient categories.
| Outcomes | ROC training (AUROC) | ROC internal validation (AUROC) | PR curve (AUPRC) | DCA |
|---|---|---|---|---|
| All-hospdeath | Adaboost (0.999) | XGBoost (0.967) | Adaboost (0.979) | XGBoost |
| All-1month | XGBoost (0.886) | Logistic (0.609) | XGBoost (0.728) | XGBoost |
| All-180death | XGBoost (0.988) | XGBoost (0.857) | XGBoost (0.960) | XGBoost |
| ICU-hospdeath | Adaboost (0.999) | XGBoost (0.963) | XGBoost (0.992) | XGBoost |
| ICU-1month | XGBoost (0.933) | Adaboost (0.687) | XGBoost (0.811) | XGBoost |
| ICU-180death | XGBoost (0.988) | XGBoost (0.864) | XGBoost (0.964) | XGBoost |
| ICU-operation-hospdeath | XGBoost (0.987) | XGBoost (0.961) | XGBoost (0.925) | XGBoost |
| ICU-operation-1month | XGBoost (0.908) | Adaboost (0.683) | XGBoost (0.758) | XGBoost |
| ICU-operation-180death | XGBoost (0.982) | XGBoost (0.828) | XGBoost (0.924) | XGBoost |
ICU, intensive care unit; ROC, Receiver Operating Characteristic curve; AUROC, area under the receiver operating characteristics curve; PR, Precision-Recall; AUPRC, area under the precision-recall curve; DCA, Decision Curve Analysis; XGBoost, eXtreme Gradient Boosting; AdaBoost, Adaptive Boosting; Logistic, Logistic regression.