Aggregated Machine Learning Approaches For The Risk-Stratification Of Children At Very Low Risk Of Clinically-Important Brain Injuries After Head Trauma

Background. In 2009, the Pediatric Emergency Care Applied Research Network (PECARN) published evidence-based guidelines for the risk-stratification of children at risk of clinically important traumatic brain injury (ciTBI). Machine learning approaches may allow for risk-stratification of patients with higher diagnostic accuracy, allowing for decreased utilization of computerized tomography (CT) in low-risk patients. Methods. We performed a secondary analysis of a public use dataset from a multicenter prospective study performed by PECARN between the years 2004-2006 from 25 North American emergency departments who presented within 24 hours of trauma, retaining only those patients with Glasgow Coma Scale scores of 14-15. Patients who were missing outcome data or who were paralyzed, intubated or sedated at the …