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

TL;DR

This study develops a transfer learning-based machine learning model to predict mortality in trauma patients, outperforming traditional regression models and effectively handling complex, non-linear risk factors in a large U.S. dataset.

Contribution

The paper introduces a novel transfer learning approach for trauma outcome prediction that does not rely on restrictive regression assumptions, demonstrating comparable or superior performance.

Findings

Model achieved high sensitivity and specificity in mortality prediction.

Excluding fall-related injuries improved adult model performance.

Model generalized well across different age groups.

Abstract

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.