Emergency Medicine

Artificial intelligence versus orthopedic surgeons as an orthopedic consultant in the emergency department.

INTRODUCTION: ChatGPT, a widely accessible AI program, has demonstrated potential in various healthcare applications, including emergency department (ED) triage, differential diagnosis, and patient education. However, its potential in providing recommendations to emergency department providers with orthopedic consultations has not been evaluated yet.

Perioperative risk assessment for emergency general surgery in those with multimorbidity or frailty.

PURPOSE OF REVIEW: This review explores advances in risk stratification tools and their applicability in identifying and managing high-risk emergency general surgery (EGS) patients.

Deep learning-assisted cellular imaging for evaluating acrylamide toxicity through phenotypic changes.

Acrylamide (AA), a food hazard generated during thermal processing, poses significant safety risks due to its toxicity. Conventional methods for AA to...

Biofilm-mediated infections; novel therapeutic approaches and harnessing artificial intelligence for early detection and treatment of biofilm-associated infections.

A biofilm is a group of bacteria that have self-produced a matrix and are grouped together in a dense population. By resisting the host's immune syste...

Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in patients with acute myocardial infarction combined with cardiogenic shock: a retrospective study based on the eICU-CRD and MIMIC-IV databases.

BACKGROUND: To evaluate the predictive utility of machine learning and nomogram in predicting in-hospital mortality in patients with acute myocardial ...

Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques.

Opioids exert their analgesic effect by binding to the µ opioid receptor (MOR), which initiates a downstream signaling pathway, eventually inhibiting ...

Explainable no-code OECD-compliant machine learning models to predict the mutagenic activity of polycyclic aromatic hydrocarbons and their radical cation metabolites.

Polycyclic aromatic hydrocarbons (PAHs) are persistent pollutants with well-known genotoxic and mutagenic effects, posing risks to ecosystems and huma...

Interpretable machine learning models for prolonged Emergency Department wait time prediction.

OBJECTIVE: Prolonged Emergency Department (ED) wait times lead to diminished healthcare quality. Utilizing machine learning (ML) to predict patient wa...

Artificial intelligence: A key fulcrum for addressing complex environmental health issues.

Environmental health (EH) is a complex and interdisciplinary field dedicated to the examination of environmental behaviours, toxicological effects, he...

State-of-the-art for automated machine learning predicts outcomes in poor-grade aneurysmal subarachnoid hemorrhage using routinely measured laboratory & radiological parameters: coagulation parameters and liver function as key prognosticators.

The objective of this study was to develop and evaluate automated machine learning (aML) models for predicting short-term (1-month) and medium-term (3...

A network toxicology and machine learning approach to investigate the mechanism of kidney injury from melamine and cyanuric acid co-exposure.

BACKGROUND: Within the past two decades, high-profile cases of melamine (MA) exposure have raised significant toxicological concerns, particularly reg...

An explainable web application based on machine learning for predicting fragility fracture in people living with HIV: data from Beijing Ditan Hospital, China.

PURPOSE: This study aimed to develop and validate a novel web-based calculator using machine learning algorithms to predict fragility fracture risk in...

Explainable machine learning models enhance prediction of PFAS bioactivity using quantitative molecular surface analysis-derived representation.

The extensive use of per- and polyfluoroalkyl substances (PFAS) in industrial and consumer products poses health risks due to their toxicity. Computat...

Eligibility for eCPR Warming in Hypothermic Cardiac Arrest: Lack of Guidelines and the Current Constraints of Artificial Intelligence in Clinical Decision-Making.

AIM OF THE STUDY: Artificial intelligence (AI) such as large language models (LLMs) tools are potential sources of information on hypothermic cardiac ...

A multi model deep net with an explainable AI based framework for diabetic retinopathy segmentation and classification.

Diabetic Retinopathy (DR) is a serious condition affecting diabetes people caused by hemorrhage in the light-sensitive retinal area. DR sufferers shou...

Compliance Evaluation with ChatGPT for Diagnosis and Treatment in Patients Brought to the ED with a Preliminary Diagnosis of Stroke.

OBJECTIVES: Chat Generative Pre-trained Transformer (ChatGPT) is a natural language processing product developed by OpenAI. Recently, the use of ChatG...

Young doctors' attrition and retention in the GP scheme: a national cohort study from Norway.

AIMS: Several countries struggle with general practitioner (GP) burnout, recruitment and retention. The reasons are not well understood. The aim of th...

Prediction of acute toxicity of organic contaminants to fish: Model development and a novel approach to identify reactive substructures.

In this study, count-based Morgan fingerprints (CMF) were employed to represent the fundamental chemical structures of contaminants, and a neural netw...

Readability of Orthopaedic Patient Educational Material: An artificial intelligence application.

BACKGROUND: This study aims to determine the efficacy of the use of artificial intelligence (AI) in rewriting orthopaedic trauma hospital patient educ...

Artificial intelligence for weight estimation in paediatric emergency care.

OBJECTIVE: To develop and validate a paediatric weight estimation model adapted to the characteristics of the Spanish population as an alternative to ...