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.

Patients' Impression of Health Care Providers' Attire in the Emergency Department.

Background The level of patient satisfaction and, ultimately, the assessment of the quality of care are greatly influenced by physicians' capacity to leave a positive impression on patients during provider-patient interactions. The way doctors dress affects how people view their care. There have been few studies on the impact of doctors' attire on patient confidence and trust.

Peptic ulcer associated with COVID-19 in Saudi Arabia.

To describe a patient's condition and clinical progress, admitted to King Abdulaziz University Hospital, Jeddah, Kingdom of Saudi Arabia with Coronaviruses disease-19 (COVID-19) infection who presented initially with gastrointestinal symptoms. The novel COVID-19 disease does not only affect the respiratory tract but also affects other parts of the body. A 23-year old male patient came to the emergency room suffering from acute abdominal pain and vomiting.