In Search of the Truth: Choice of Ground Truth for Predictive Modeling of Trauma Team Activation in Pediatric Trauma.

Background: Assigning trauma team activation (TTA) levels for trauma patients is a classification task that machine learning models can help optimize. However, performance is dependent on the "ground-truth" labels used for training. Our purpose was to investigate 2 ground truths, the Cribari matrix and the Need for Trauma Intervention (NFTI), for labeling training data.

Machine Learning Improves the Accuracy of Trauma Team Activation Level Assignments in Pediatric Patients.

Background: The assignment of trauma team activation levels can be conceptualized as a classification task. Machine learning models can be used to optimize classification predictions. Our purpose was to demonstrate proof-of-concept for a machine learning tool for predicting trauma team activation levels in pediatric patients with traumatic injuries.