@article {Cardosie100407, author = {Joshua David Cardosi and Herman Shen and Jonathan I Groner and Megan Armstrong and Henry Xiang}, title = {Machine learning for outcome predictions of patients with trauma during emergency department care}, volume = {28}, number = {1}, elocation-id = {e100407}, year = {2021}, doi = {10.1136/bmjhci-2021-100407}, publisher = {BMJ Specialist Journals}, abstract = {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. The mortality prediction model was evaluated on its sensitivity, specificity, area under receiver operating curve (AUC), positive and negative predictive value, and Matthews correlation coefficient.Results Our final dataset consisted of 2 007 485 patient visits (36.45\% female, mean age of 45), 8198 (0.4\%) of which resulted in mortality. Our model achieved AUC and sensitivity-specificity gap of 0.86 (95\% CI 0.85 to 0.87), 0.44 for children and 0.85 (95\% CI 0.85 to 0.85), 0.44 for adults. The all ages model characteristics indicate it generalised, with an AUC and gap of 0.85 (95\% CI 0.85 to 0.85), 0.45. Excluding fall injuries weakened the child model (AUC 0.85, 95\% CI 0.84 to 0.86) but strengthened adult (AUC 0.87, 95\% CI 0.87 to 0.87) and all ages (AUC 0.86, 95\% CI 0.86 to 0.86) models.Conclusions Our machine learning model demonstrates similar performance to contemporary machine learning models without requiring restrictive criteria or extensive medical expertise. These results suggest that machine learning models for trauma outcome prediction can generalise to patients with trauma across the USA and may be able to provide decision support to medical providers in any healthcare setting.Data may be obtained from a third party and are not publicly available. The National Trauma Data Bank dataset consists of deidentified patient data. It is made available by the American College of Surgeons for researchers meeting their eligibility criteria.}, URL = {https://informatics.bmj.com/content/28/1/e100407}, eprint = {https://informatics.bmj.com/content/28/1/e100407.full.pdf}, journal = {BMJ Health \& Care Informatics} }