Predicting Patient Outcomes After Car Crashes: A Machine Learning Perspective

The rapid assessment for life threatening injuries is essential to effective trauma care. Therefore, there is no greater potential to impact human lives after trauma than by hastening our ability to obtain data on the severity of patients’ injuries. With the abundance of data collected by vehicles before crashes, there is an opportunity to learn how these data can be used in the field to predict clinical outcomes. We aim to train a machine learning model that predicts for severe injuries after motor vehicle crashes, using vehicular data obtained from automotive crashes. We will present our preliminary work towards an AHRQ Patient-Centered Outcomes Research (PCOR) grant application and look forward to discussing our initial Aims. 

Zoom information will be sent to e-HAIL members.