Machine learning to predict extubation outcome in premature infants.

Abstract

Though treatment of the ventilated premature infant has experienced many advances over the past decades, determining the best time point for extubation of these infants remains challenging and the incidence of extubation failures largely unchanged. The objective was to provide clinicians with a decision-support tool to determine whether to extubate a mechanically ventilated premature infant by using a set of machine learning algorithms on a dataset assembled from 486 premature infants receiving mechanical ventilation. Algorithms included artificial neural networks (ANN), support vector machine (SVM), naïve Bayesian classifier (NBC), boosted decision trees (BDT), and multivariable logistic regression (MLR). Results for ANN, MLR, and NBC were satisfactory (area under the curve [AUC]: 0.63-0.76); however, SVM and BDT consistently showed poor performance (AUC ~0.5). Complex medical data such as the data set used for this study require further preprocessing steps before prediction models can be developed that achieve similar or better performance than clinicians.