Seeing the forest beyond the trees: Predicting survival in burn patients with machine learning

Background: This study aims to identify predictors of survival for burn patients at the patient and hospital level using machine learning techniques. Methods: The HCUP SID for California, Florida and New York were used to identify patients admitted with a burn diagnosis and merged with hospital data from the AHA Annual Survey. Random forest and stochastic gradient boosting (SGB) were used to identify predictors of survival at the patient and hospital level from the top performing model. Results: We analyzed 31,350 patients from 670 hospitals. SGB (AUC 0.93) and random forest (AUC 0.82) best identified patient factors such as age and absence of renal failure (p < 0.001) and hospital factors such as full time residents (p < 0.001) and nurses (p = 0.004) to be associated with increased survival. Conclusions: Patient and hospital factors are predictive of survival in burn patients. It is difficult to control patient factors, but hospital factors can inform decisions about where burn patients should be treated. (c) 2017 Elsevier Inc. All rights reserved.