Predicting the need for CT imaging in children with minor head injury using an ensemble of Naive Bayes classifiers

摘要

ObjectiveUsing an automatic data-driven approach, this paper develops a prediction model that achieves more balanced performance (in terms of sensitivity and specificity) than the Canadian Assessment of Tomography for Childhood Head Injury (CATCH) rule, when predicting the need for computed tomography (CT) imaging of children after a minor head injury.