  Trauma mortality results from a multitude of non-linear dependent risk
factors including patient demographics, injury characteristics, medical care
provided, and characteristics of medical facilities; yet traditional approach
attempted to capture these relationships using rigid regression models. We
hypothesized that a transfer learning based machine learning algorithm could
deeply understand a trauma patient's condition and accurately identify
individuals at high risk for mortality without relying on restrictive
regression model criteria. Anonymous patient visit data were obtained from
years 2007-2014 of the National Trauma Data Bank. Patients with incomplete
vitals, unknown outcome, or missing demographics data were excluded. All
patient visits occurred in U.S. hospitals, and of the 2,007,485 encounters that
were retrospectively examined, 8,198 resulted in mortality (0.4%). The machine
intelligence model was evaluated on its sensitivity, specificity, positive and
negative predictive value, and Matthews Correlation Coefficient. Our model
achieved similar performance in age-specific comparison models and generalized
well when applied to all ages simultaneously. While testing for confounding
factors, we discovered that excluding fall-related injuries boosted performance
for adult trauma patients; however, it reduced performance for children. The
machine intelligence model described here demonstrates similar performance to
contemporary machine intelligence models without requiring restrictive
regression model criteria or extensive medical expertise.
