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.

Role of serum lactate as prognostic marker of mortality among emergency department patients with multiple conditions: A systematic review.

Objective: In the Emergency Department, the physician's purpose is to rapidly and accurately diagnose the severity of a patient's condition so that proper treatment can be administered. Therefore, this study pays close attention to the association and prognostic value of blood lactate on different disease outcomes by systematically reviewing recently published studies.

Methods: The search strategy was developed based on searches in the year 2022 for potential publications including original articles, case reports, and reviews using the following web databases google scholar, PubMed, and Science Direct.