Machine Learning for Outcome Prediction in Electroencephalograph (EEG)-Monitored Children in the Intensive Care Unit

Abstract

The aim of this study was to evaluate the performance of models predicting in-hospital mortality in critically ill children undergoing continuous electroencephalography (cEEG) in the intensive care unit (ICU). We evaluated the performance of machine learning algorithms for predicting mortality in a database of 414 critically ill children undergoing cEEG in the ICU. The area under the receiver operating characteristic curve (AUC) in the test subset was highest for stepwise selection/elimination models (AUC = 0.82) followed by least absolute shrinkage and selection operator (LASSO) and support vector machine with linear kernel (AUC = 0.79), and random forest (AUC = 0.71). The explanatory models had the poorest discriminative performance (AUC = 0.63 for the model without considering etiology and AUC = 0.45 for the model considering etiology). Using few variables and a relatively small number of patients, machine learning techniques added information to explanatory models for prediction of in-hospital mortality.