Predicting triage of pediatric patients in the emergency department using machine learning approach.

Background: The efficient performance of an Emergency Department (ED) relies heavily on an effective triage system that prioritizes patients based on the severity of their medical conditions. Traditional triage systems, including those using the Canadian Triage and Acuity Scale (CTAS), may involve subjective assessments by healthcare providers, leading to potential inconsistencies and delays in patient care.

Objective: This study aimed to evaluate six Machine Learning (ML) models K-Nearest Neighbors (KNN), Support Vector Machine (SCM), Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB), and Light GBM (Light Gradient Boosting Machine) for triage prediction in the King Abdulaziz University Hospital using the CTAS framework.

Potentially lethal ACE-inhibitor-induced angioedema in a child.

We report a case of a 9-year-old female with known end-stage kidney disease who presented with sudden onset tongue swelling. A diagnosis of angiotensin-converting enzyme inhibitor-induced angioedema related to bradykinin accumulation was made. Her symptoms resolved shortly after discontinuation of captopril.