Emergency Medicine

Artificial intelligence and machine learning for hemorrhagic trauma care.

Artificial intelligence (AI), a branch of machine learning (ML) has been increasingly employed in th...

Spatial and deep learning analyses of urban recovery from the impacts of COVID-19.

This study investigates urban recovery from the COVID-19 pandemic by focusing on three main types of...

Modeling acute care utilization: practical implications for insomnia patients.

Machine learning models can help improve health care services. However, they need to be practical to...

Conventional Versus Artificial Intelligence-Assisted Interpretation of Chest Radiographs in Patients With Acute Respiratory Symptoms in Emergency Department: A Pragmatic Randomized Clinical Trial.

OBJECTIVE: It is unknown whether artificial intelligence-based computer-aided detection (AI-CAD) can...

User Feedback on the Use of a Natural Language Processing Application to Screen for Suicide Risk in the Emergency Department.

Suicide is the 10th leading cause of death in the USA and globally. Despite decades of research, the...

Machine learning to improve frequent emergency department use prediction: a retrospective cohort study.

Frequent emergency department use is associated with many adverse events, such as increased risk for...

Development and evaluation of deep learning algorithms for assessment of acute burns and the need for surgery.

Assessment of burn extent and depth are critical and require very specialized diagnosis. Automated i...

Deep learning-based hemorrhage detection for diabetic retinopathy screening.

Diabetic retinopathy is a retinal compilation that causes visual impairment. Hemorrhage is one of th...

Improved neural network for predicting blood donations based on two emergent factors.

BACKGROUND: Blood donation forecasting is a critical part of blood supply chain management. However,...

XAIRE: An ensemble-based methodology for determining the relative importance of variables in regression tasks. Application to a hospital emergency department.

Nowadays it is increasingly important in many applications to understand how different factors influ...

Benchmarking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence.

The SARS-CoV-2 pandemic highlighted the need for software tools that could facilitate patient triage...

Triaging: Another Vital Application of the Deep Learning Technique on Chest Radiographs at the Emergency Department.

Deep Learning Analysis of Chest Radiographs to Triage Patients with Acute Chest Pain Syndrome.

Background Patients presenting to the emergency department (ED) with acute chest pain (ACP) syndrome...

Development and External Validation of a Deep Learning Algorithm to Identify and Localize Subarachnoid Hemorrhage on CT Scans.

BACKGROUND AND OBJECTIVES: In medical imaging, a limited number of trained deep learning algorithms ...

Autonomous Driving Control Based on the Technique of Semantic Segmentation.

Advanced Driver Assistance Systems (ADAS) are only applied to relatively simple scenarios, such as h...

Machine Learning as an Adjunct to Traditional Triage in the Emergency Department.

Transfer learning-based ensemble convolutional neural network for accelerated diagnosis of foot fractures.

The complex shape of the foot, consisting of 26 bones, variable ligaments, tendons, and muscles lead...

Clinician and computer: a study on doctors' perceptions of artificial intelligence in skeletal radiography.

BACKGROUND: Traumatic musculoskeletal injuries are a common presentation to emergency care, the firs...

The effect of the pandemic on European narratives on smart cities and surveillance.

This article presents an analysis of European smart city narratives and how they evolved under the p...

Artificial intelligence (AI) acceptance in primary care during the coronavirus pandemic: What is the role of patients' gender, age and health awareness? A two-phase pilot study.

BACKGROUND: Artificial intelligence (AI) is steadily entering and transforming the health care and P...

Non-coding deep learning models for tomato biotic and abiotic stress classification using microscopic images.

Plant disease classification is quite complex and, in most cases, requires trained plant pathologist...