Machine Intelligence for Outcome Predictions of Trauma Patients During Emergency Department Care

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 . 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 . We discovered that excluded fall- related injuries boosted . However, we also found that excluding Fall-related injury boosted performance reduced performance for adults, however, to boost performance for

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Keywords : performance - trauma - patient - fall - machine -

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