The American journal of emergency medicine
39731895
BACKGROUND: The number of emergency department (ED) visits has been on steady increase globally. Artificial Intelligence (AI) technologies, including Large Language Model (LLMs)-based generative AI models, have shown promise in improving triage accur...
OBJECTIVE: Evaluate the accuracy and reliability of various generative artificial intelligence (AI) models (ChatGPT-3.5, ChatGPT-4.0, T5, Llama-2, Mistral-Large, and Claude-3 Opus) in predicting Emergency Severity Index (ESI) levels for pediatric eme...
BACKGROUND: Dyspnoea is one of the emergency department's (ED) most common and deadly chief complaints, but frequently misdiagnosed and mistreated. We aimed to design a diagnostic decision support which classifies dyspnoeic ED visits into acute heart...
OBJECTIVE: Trauma resuscitation is the initial evaluation and management of injured patients in the emergency department. This time-critical process requires the simultaneous pursuit of multiple resuscitation goals. Recognizing whether the required g...
World journal of emergency surgery : WJES
39716296
BACKGROUND: Acute abdominal pain (AAP) constitutes 5-10% of all emergency department (ED) visits, with appendicitis being a prevalent AAP etiology often necessitating surgical intervention. The variability in AAP symptoms and causes, combined with th...
BMC medical informatics and decision making
39696224
BACKGROUND: Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of ED patient arrivals can help management to better allocate staff and medical resources. In this study, we investigate the use of cal...
INTRODUCTION: Delayed neurological sequelae is a common complication following carbon monoxide poisoning, which significantly affects the quality of life of patients with the condition. We aimed to develop a machine learning-based prediction model to...
INTRODUCTION: Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the...
The study aimed to develop and validate a sepsis prediction model using structured electronic medical records (sEMR) and machine learning (ML) methods in emergency triage. The goal was to enhance early sepsis screening by integrating comprehensive tr...
European heart journal. Acute cardiovascular care
39787045
AIMS: Acute heart failure (AHF) poses significant diagnostic challenges in the emergency room (ER) because of its varied clinical presentation and limitations of traditional diagnostic methods. This study aimed to develop and evaluate a deep learning...