Machine learning for outcome predictions of patients with trauma during emergency department care.

Objectives: To develop and evaluate a machine learning model for predicting patient with trauma mortality within the US emergency departments.

Methods: This was a retrospective prognostic study using deidentified patient visit data from years 2007 to 2014 of the National Trauma Data Bank. The predictive model intelligence building process is designed based on patient demographics, vital signs, comorbid conditions, arrival mode and hospital transfer status.