Predicting Prolonged Respiratory Failure via Unsupervised and Supervised Machine Learning Models

Presentation Time: 04:30 PM - 04:45 PM

Abstract Keywords: Critical Care, Machine Learning, Clinical Decision Support
Primary Track: Applications

Predicting prolonged respiratory failure (PRF, patients requiring intubation ≥14 days for this task) in patients requiring mechanical ventilation is challenging but beneficial for treatment planning. This study applies supervised and unsupervised machine learning methods on an ICU cohort with confirmed/suspected pneumonia for PRF prediction. Group-based multivariate trajectory modeling (GBMT) on five ventilator parameters collected during the first five days of intubation identified four groups that represent unique phenotypes and are predictive of PRF. Multivariate Time Series Transformer (MVTS) showed superior discrimination of PRF using time-series clinical data in comparison with XGBoost on baseline data.

Speaker(s):
Yanyi Ding, MS
Northwestern University

Author(s):
Yanyi Ding, MS - Northwestern University; Meghan Hutch, BS - Northwestern University - Feinberg School of Medicine; Thomas Stoeger, PhD - Northwestern University; Yuan Luo, PhD - Northwestern University; Catherine Gao, MD - Northwestern;