Fig 1.
Inclusion and exclusion criteria.
“n” corresponds to the number of triages.
Table 1.
Demographic variables and a subset of features are summarized for emergency department patients with and without the composite outcome.
Fig 2.
Models performance using only clinical variables (on the left) and including the chief complaint (on the right).
For plots on the left, each model performance (AUROC, AUPRC) is: logistic regression (0.93, 0.17), random forests (0.95, 0.21) and extreme gradient boosting (0.96, 0.25). For plots on the right, each model performance (AUROC, AUPRC) is: logistic regression (0.95, 0.25), random forests (0.94, 0.20) and extreme gradient boosting (0.96, 0.30). AUROC—Area under the ROC curve. AUPRC—Area under the precision recall curve.
Fig 3.
Calibration curves for the models using only clinical variables (on the left) and including the chief complaint (on the right).
Fig 4.
Calibration curve for the XGBoost model using clinical variables and the chief complaint with isotonic calibration.
Table 2.
Performance results for the XGBoost calibrated model using clinical variables and chief complaint against the reference model (triage priority) and respective hyperparameters.
AUROC was the performance measure for hyperparameter tuning and best model selection in train. The hyperparameters not mentioned in the table were the default in XGBClassifier from Python version 3.7.
Table 3.
Brier Skill Score (BSS) for the models using clinical variables and chief complaint against a unskilled reference model using triage priority.
Reference Brier Score = 0.005.
Fig 5.
Relative importance of predictors obtained with XGBoost using all available variables.
Exams are prescribed at the time of triage.
Fig 6.
Risk probability for incorrect (on the top) and correct (on the bottom) classifications obtained with extreme gradient boosting model using clinical variables and chief complaint.
A zoom-in is presented for the predicted risk probability for the plots on the right. The classification threshold is 0.00824.
Fig 7.
Predicted risk probability for false negative classifications discriminated by triage priority obtained with XGBoost model using clinical variables and chief complaint.
Fig 8.
Predicted risk probability for false positive classifications discriminated by triage priority obtained with XGBoost model using clinical variables and chief complaint.