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

TL;DR

This paper introduces a machine learning approach using undersampled bagging of decision trees to analyze cardiorespiratory data, aiming to predict extubation readiness in extremely preterm infants with improved accuracy.

Contribution

The study develops a novel ensemble method combining undersampling and bagging of decision trees, incorporating clinical knowledge to enhance prediction of extubation outcomes.

Findings

Identified 71% of infants who failed extubation.

Achieved 78% success detection rate.

Demonstrated effectiveness of the method in clinical data.

Abstract

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%.