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.

Prediction of COVID-19 Hospitalization and Mortality Using Artificial Intelligence.

Background: COVID-19 has had a substantial influence on healthcare systems, requiring early prognosis for innovative therapies and optimal results, especially in individuals with comorbidities. AI systems have been used by healthcare practitioners for investigating, anticipating, and predicting diseases, through means including medication development, clinical trial analysis, and pandemic forecasting. This study proposes the use of AI to predict disease severity in terms of hospital mortality among COVID-19 patients.

Non-painful severe variant form of eruptive lingual papillitis: A case report and literature review.

Eruptive lingual papillitis is a common benign disorder manifested by inflammation of fungiform papillae on the dorsolateral surface of the tongue. Several variants of lingual papillitis have been reported since 1997, most or all of them with painful erythematous papules. Here we report a case of 6 years old girl child with non-painful severe variant form of eruptive lingual papillitis presented to the emergency department.