Risk assessment for intra-abdominal injury following blunt trauma in children: Derivation and validation of a machine learning model.

Background: Computed tomography is the criterion standard for diagnosing intra-abdominal injury (IAI) but is expensive and risks radiation exposure. The Pediatric Emergency Care Applied Research Network (PECARN) model identifies children at low risk of IAI requiring intervention (IAI-I) in whom computed tomography may be omitted but does not provide an individualized risk assessment to positively predict IAI-I. We sought to apply machine learning algorithms to the PECARN blunt abdominal trauma (BAT) data set experimentally to create models for predicting both the presence and absence of IAI-I for pediatric BAT victims.

Derivation and Validation of a Simplified Clinical Prediction Rule for Identifying Children at Increased Risk for Clinically Important Traumatic Brain Injuries Following Minor Blunt Head Trauma.

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.