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Our work focuses on the problem of predicting the transfer of pediatricpatients from the general ward of a hospital to the pediatric intensive careunit. Using data collected over 5.5 years from the electronic health records oftwo medical facilities, we develop classifiers based on adaptive boosting andgradient tree boosting. We further combine these learned classifiers into anensemble model and compare its performance to a modified pediatric earlywarning score (PEWS) baseline that relies on expert defined guidelines. Togauge model generalizability, we perform an inter-facility evaluation where wetrain our algorithm on data from one facility and perform evaluation on ahidden test dataset from a separate facility. We show that improvements arewitnessed over the PEWS baseline in accuracy (0.77 vs. 0.69), sensitivity (0.80vs. 0.68), specificity (0.74 vs. 0.70) and AUROC (0.85 vs. 0.73).

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