  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).
