UC Davis

Objective

To develop a simplified clinical prediction tool for identifying children with clinically important traumatic brain injuries (ciTBIs) after minor blunt head trauma by applying machine learning to the previously reported Pediatric Emergency Care Applied Research Network dataset.

Study design

The deidentified dataset consisted of 43 399 patients <18 years old who presented with blunt head trauma to 1 of 25 pediatric emergency departments between June 2004 and September 2006. We divided the dataset into derivation (training) and validation (testing) subsets; 4 machine learning algorithms were optimized using the training set. Fitted models used the test set to predict ciTBI and these predictions were compared statistically with the a priori (no information) rate.

Results

None of the 4 machine learning models was superior to the no information rate. Children without clinical evidence of a skull fracture and with Glasgow Coma Scale scores of 15 were at the lowest risk for ciTBIs (0.48%; 95% CI 0.42%-0.55%).

Conclusions

Machine learning algorithms were unable to produce a more accurate prediction tool for ciTBI among children with minor blunt head trauma beyond the absence of clinical evidence of skull fractures and having Glasgow Coma Scale scores of 15.