Journal of Forensic Biomechanics

Abstrait

Machine Learning Methods to Predict Survival in Patients Following Traumatic Aortic Injury

Nisreen Shiban, Henry Zhan, Nima Kokabi, Jamlik-Omari Johnson, Tarek Hanna, Justin Schrager, Judy Gichoya, Imon Banerjee, Hari Trivedi, Joshua Gaul, Andrew Elhabr

The National Trauma Data Bank (NTDB) is a resource of diagnostic, treatment, and outcomes information in trauma patients. We leverage the NTDB and machine learning techniques to predict survival following traumatic aortic injury. We create two predictive models using the NTDB–1) using all data and, 2) using only data available in the first hour after arrival (prospective data). Seven discriminative model types were tested before and after feature engineering to reduce dimensionality. The top performing model was XG Boost, achieving an overall accuracy of 0.893 using all data and 0.855 using prospective data. Feature engineering improved performance of all models. Glasgow Coma Scale score was the most important factor for survival, and thoracic endovascular aortic repair was more common in patients that survived. Smoking, pneumonia, and urinary tract infection predicted poor survival. We also note concerning disparities in outcomes for black and uninsured patients that may reflect differences in care.