Machine learning to identify attributes that predict patients who leave without being seen in a pediatric emergency department.

Our analysis showed that machine learning models can be used on administrative data to predict patients who LWBS in a Canadian pediatric ED. From 16 variables, we identified the five most influential model attributes. System-level interventions to improve patient flow have shown promise for reducing LWBS in some centres. Predicting patients likely to LWBS raises the possibility of individual patient-level interventions to mitigate LWBS.