Predicting Cervical Spine Injury in Pediatric Trauma Patients


Cervical spine injuries (CSI) are a major concern in pediatric trauma patients. Despite its low incidence, the consequences of missed injuries and challenges in evaluating young non-verbal patients have resulted in high reliance on imaging for injury clearance. Imaging, and particularly CT scans, carries its own set of risks including radiation-induced carcinogenesis. In this work, we leverage optimization and machine learning techniques to predict CSI incidence based on clinical exam findings. We propose an interpretable injury clearance protocol that achieves high identification of injured patients while avoiding a significant proportion of unnecessary imaging.

Sponsored Session
INFORMS Annual Meeting 2018