An Ensemble Boosting Model for Predicting Transfer to the Pediatric Intensive Care Unit

TL;DR

This study develops an ensemble boosting model using electronic health record data to predict pediatric patient transfers to intensive care, outperforming traditional scoring methods across multiple metrics.

Contribution

The paper introduces an ensemble boosting approach that improves prediction accuracy and generalizability for pediatric ICU transfers over existing scoring systems.

Findings

Ensemble boosting outperforms PEWS in accuracy, sensitivity, specificity, and AUROC.

Model trained on one facility's data generalizes well to another facility.

Significant improvements demonstrate potential for clinical decision support.

Abstract

Our work focuses on the problem of predicting the transfer of pediatric patients from the general ward of a hospital to the pediatric intensive care unit. Using data collected over 5.5 years from the electronic health records of two medical facilities, we develop classifiers based on adaptive boosting and gradient tree boosting. We further combine these learned classifiers into an ensemble model and compare its performance to a modified pediatric early warning score (PEWS) baseline that relies on expert defined guidelines. To gauge model generalizability, we perform an inter-facility evaluation where we train our algorithm on data from one facility and perform evaluation on a hidden test dataset from a separate facility. We show that improvements are witnessed over the PEWS baseline in accuracy (0.77 vs. 0.69), sensitivity (0.80 vs. 0.68), specificity (0.74 vs. 0.70) and AUROC (0.85 vs. 0.73).