Emergency Medicine

Diagnostic test accuracy of externally validated convolutional neural network (CNN) artificial intelligence (AI) models for emergency head CT scans - A systematic review.

BACKGROUND: The surge in emergency head CT imaging and artificial intelligence (AI) advancements, es...

Quantitative prediction of postpartum hemorrhage in cesarean section on machine learning.

BACKGROUND: Cesarean section-induced postpartum hemorrhage (PPH) potentially causes anemia and hypov...

Cardiogenic Shock Update: New Trials, Evolving Management Paradigms, and Artificial Intelligence.

Machine learning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis.

PURPOSE: Early identification of hematoma enlargement and persistent hematoma expansion (HE) in pati...

Assessing screw length impact on bone strain in proximal humerus fracture fixation via surrogate modelling.

A high failure rate is associated with fracture plates in proximal humerus fractures. The causes of ...

Deep Learning Model for Automatic Identification and Classification of Distal Radius Fracture.

Distal radius fracture (DRF) is one of the most common types of wrist fractures. We aimed to constru...

Machine learning algorithms integrate bulk and single-cell RNA data to unveil oxidative stress following intracerebral hemorrhage.

BACKGROUND: Increased oxidative stress (OS) activity following intracerebral hemorrhage (ICH) had si...

Managing low-acuity patients in an Emergency Department through simulation-based multiobjective optimization using a neural network metamodel.

This paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a strategy ofte...

Preface to the special issue of Food and Chemical Toxicology on "New approach methodologies and machine learning in food safety and chemical risk assessment: Development of reproducible, open-source, and user-friendly tools for exposure, toxicokinetic, and toxicity assessments in the 21st century".

This Special Issue contains articles on applications of various new approach methodologies (NAMs) in...

Interpretable machine learning predicts postpartum hemorrhage with severe maternal morbidity in a lower-risk laboring obstetric population.

BACKGROUND: Early identification of patients at increased risk for postpartum hemorrhage (PPH) assoc...

Prediagnosis recognition of acute ischemic stroke by artificial intelligence from facial images.

Stroke is a major threat to life and health in modern society, especially in the aging population. S...

A Novel Machine Learning Model for Predicting Stroke-Associated Pneumonia After Spontaneous Intracerebral Hemorrhage.

BACKGROUND: Pneumonia is one of the most common complications after spontaneous intracerebral hemorr...

Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms.

In contemporary society, depression has emerged as a prominent mental disorder that exhibits exponen...

Prognostic biomarkers of intracerebral hemorrhage identified using targeted proteomics and machine learning algorithms.

Early prognostication of patient outcomes in intracerebral hemorrhage (ICH) is critical for patient ...

Machine learning-based model for predicting outcomes in cerebral hemorrhage patients with leukemia.

BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) in leukemia patients progresses rapidly with h...

ChatGPT in medicine: prospects and challenges: a review article.

It has been a year since the launch of Chat Generator Pre-Trained Transformer (ChatGPT), a generativ...

Machine learning based peri-surgical risk calculator for abdominal related emergency general surgery: a multicenter retrospective study.

BACKGROUND: Currently, there is a lack of ideal risk prediction tools in the field of emergency gene...

Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis.

BACKGROUND: Deep learning (DL)-assisted detection and segmentation of intracranial hemorrhage stroke...

Leveraging graph neural networks for supporting automatic triage of patients.

Patient triage is crucial in emergency departments, ensuring timely and appropriate care based on co...

Appropriate use of blood cultures in the emergency department through machine learning (ABC): study protocol for a randomised controlled non-inferiority trial.

INTRODUCTION: The liberal use of blood cultures in emergency departments (EDs) leads to low yields a...

A novel case-finding strategy based on artificial intelligence for the systematic identification and management of individuals with osteoporosis or at varying risk of fragility fracture.

UNLABELLED: An artificial intelligence-based case-finding strategy has been developed to systematica...