A machine learning model for prediction of successful extubation in patients admitted to the intensive care unit

Background and objective: Successful weaning from mechanical ventilation is important for patients admitted to intensive care units (ICUs); however, models for predicting real-time weaning outcomes remain inadequate. Therefore, this study was designed to develop a machine learning model using time series ventilator-derived parameters with good accuracy for predicting successful extubation. Methods Patients with mechanical ventilation between August 2015 and November 2020 admitted Yuanlin Christian Hospital in Taiwan were retrospectively included. The ventilator-derived parameter time series dataset was collected before extubation. Recursive Feature Elimination (RFE) was applied to choose the most important features. Machine learning models of logistic regression, random forest (RF), and support vector machine were adopted for predicting extubation outcomes. In addition, the synthetic minority oversampling technique (SMOTE) was employed to address the data imbalance problem. Area under receiver operating characteristic (AUC), F1 score, and accuracy along with 10-fold cross-validation were used to evaluate prediction performance. Results In this study, 233 patients were included, of whom 28 (12.0%) failed extubation. Moreover, the six ventilatory variables per 180-s dataset had the optimal feature importance. The RF exhibited better performance than others with an AUC of 0.976 (95% confidence interval [CI], 0.975–0.976), an accuracy of 94.0% (95% CI, 93.8–94.3%), and an F1 score of 95.8% (95% CI, 95.7–96.0%). The difference in performance between the RF with original and SMOTE dataset was small. Conclusion The RF model demonstrated good performance for predicting successful extubation of mechanically ventilated patients. This algorithm makes a precise real-time extubation outcome prediction for a patient at different time points.