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Table 3 Evaluation report using the best model with the SMOTE preprocessing algorithm on the outcomes of testing dataset of older ED patients with influenza

From: Predicting outcomes in older ED patients with influenza in real time using a big data-driven and machine learning approach to the hospital information system

Outcome Number Negative
outcome
Positive outcome Accuracy Sensitivity Specificity PPV NPV AUC
Hospitalization (random forest) 5508 2901 2607 0.769 0.744 0.791 0.762 0.775 0.840
Pneumonia (random forest) 5508 3431 2077 0.679 0.681 0.679 0.562 0.778 0.765
Sepsis or septic shock (random forest) 5508 5201 307 0.795 0.750 0.798 0.179 0.982 0.857
ICU admission (XGBoost) 5508 5449 59 0.912 0.722 0.914 0.085 0.997 0.902
In-hospital mortality (logistic regression) 5508 5387 121 0.816 0.806 0.816 0.089 0.995 0.889
  1. SMOTE Synthetic minority oversampling technique; ED Emergency department; PPV Positive predictive value; NPV Negative predictive value; AUC Area under the curve; ICU Intensive care unit; XGBoost Extreme Gradient Boosting