WORKSHOP by Mousumi Banerjee (University of Michigan)

WORKSHOP by Mousumi Banerjee (University of Michigan) on "A machine learning based tandem approach to predict extubation failure in pediatric intensive care unit "

Abstract :

Pediatric cardiac critical care providers are often challenged with the equally important but often conflicting goals of minimizing patients’ exposure to mechanical ventilation and preventing extubation failure. Extubation failures have been associated with adverse outcomes including cardiac arrest and mortality. Reliable measures of extubation readiness, while validated in adult patients, remain elusive in pediatric cardiac critical care. Patients in the cardiac intensive care unit (CICU) have heterogeneous pathophysiology, and failure to breathe without assistance from a ventilator can be the result of primary respiratory or cardiac failure, or a mixed etiology. Physicians and nurses need prediction tools to help with clinical decision making when assessing children in the CICU for extubation readiness. 
This paper develops a prediction tool using large-scale “shallow” data from a clinical registry of over 50 institutions from North America (Pediatric Cardiac Critical Care Consortium: PC4), combined with small-scale “deep” data from CICU monitors and devices at 1 minute intervals. The latter data source allows the opportunity to study physiologic parameters during the key period when patients are evaluated for extubation readiness. We develop a tandem machine learning based approach to combine large-scale, shallow data with small-scale, deep data to improve prediction. The idea is to perform sequential classification: first using widely available covariates for risk stratification and subsequently refining prediction using deep data. Time series models are used to extract features from the deep data. We develop a novel framework that is time and cost-effective, for identifying patient subgroups that would most benefit from a second-stage prediction refinement using the deep data. Final tandem prediction is obtained by combining predictions from both the first and second stage classifiers. Our proposed method yields a classifier with improved prediction accuracy for predicting extubation failure in the CICU.

Workshop given online by TEAMS here : TEAMS