1 research outputs found
Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants
Extremely preterm infants often require endotracheal intubation and
mechanical ventilation during the first days of life. Due to the detrimental
effects of prolonged invasive mechanical ventilation (IMV), clinicians aim to
extubate infants as soon as they deem them ready. Unfortunately, existing
strategies for prediction of extubation readiness vary across clinicians and
institutions, and lead to high reintubation rates. We present an approach using
Random Forest classifiers for the analysis of cardiorespiratory variability to
predict extubation readiness. We address the issue of data imbalance by
employing random undersampling of examples from the majority class before
training each Decision Tree in a bag. By incorporating clinical domain
knowledge, we further demonstrate that our classifier could have identified 71%
of infants who failed extubation, while maintaining a success detection rate of
78%.Comment: Published in: 2018 40th Annual International Conference of the IEEE
Engineering in Medicine and Biology Society (EMBC