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Extremely preterm infants often require endotracheal intubation andmechanical ventilation during the first days of life. Due to the detrimentaleffects of prolonged invasive mechanical ventilation (IMV), clinicians aim toextubate infants as soon as they deem them ready. Unfortunately, existingstrategies for prediction of extubation readiness vary across clinicians andinstitutions, and lead to high reintubation rates. We present an approach usingRandom Forest classifiers for the analysis of cardiorespiratory variability topredict extubation readiness. We address the issue of data imbalance byemploying random undersampling of examples from the majority class beforetraining each Decision Tree in a bag. By incorporating clinical domainknowledge, we further demonstrate that our classifier could have identified 71%of infants who failed extubation, while maintaining a success detection rate of78%.

Undersampling and Bagging of Decision Trees in the Analysis of  Cardiorespiratory Behavior for the Prediction of Extubation Readiness in  Extremely Preterm Infants