Emergency Medicine

Hip Fracture Patient Pathways and Agent-based Modelling

Increased healthcare demand is significantly straining European services. Digital solutions including advanced modelling techniques offer a promising solution to optimising patient flow without impacting day-to-day healthcare provision. In this work we outline an ongoing project that aims to optimise healthcare resources using agent-based simulations.

Management and Visualization Tools for Emergency Medical Services

This paper describes an online tool for the visualization of medical emergency locations, randomly generated sample paths of medical emergencies, and the animation of ambulance movements under the control of various dispatch methods in response to these emergencies. The tool incorporates statistical models for forecasting emergency locations and call arrival times, the simulation of emergency ar...

Detection and classification of mandibular fractures in panoramic radiography using artificial intelligence.

OBJECTIVES: This study evaluated the performance of the YOLOv5 deep learning model in detecting different mandibular fracture types in panoramic image...

Deep Learning to Detect Intracranial Hemorrhage in a National Teleradiology Program and the Impact on Interpretation Time.

The diagnostic performance of an artificial intelligence (AI) clinical decision support solution for acute intracranial hemorrhage (ICH) detection was...

Artificial Intelligence in Military Medicine.

Artificial intelligence (AI) has garnered significant attention for its pivotal role in the national security and health care sectors. However, its ut...

DIC2Abaqus: Calculating mixed-mode stress intensity factors from 2D and 3D-stereo displacement fields

Integrating experimental data into simulations is crucial for predicting material behaviour, especially in fracture mechanics. Digital Image Correla...

[Study on automatic and rapid diagnosis of distal radius fracture by X-ray].

This article aims to combine deep learning with image analysis technology and propose an effective classification method for distal radius fracture ty...

Learning from the few: Fine-grained approach to pediatric wrist pathology recognition on a limited dataset

Wrist pathologies, {particularly fractures common among children and adolescents}, present a critical diagnostic challenge. While X-ray imaging rema...

Harnessing the Power of Machine Learning and Electronic Health Records to Support Child Abuse and Neglect Identification in Emergency Department Settings.

Emergency departments (EDs) are pivotal in detecting child abuse and neglect, but this task is often complex. Our study developed a machine learning m...

Exploring Hospital Overcrowding with an Explainable Time-to-Event Machine Learning Approach.

Emergency department (ED) overcrowding is a complex problem that is intricately linked with the operations of other hospital departments. Leveraging E...

Remote Monitoring, AI, Machine Learning and Mobile Ultrasound Integration upon 5G Internet in the Prehospital Care to Support the Golden Hour Principle and Optimize Outcomes in Severe Trauma and Emergency Surgery.

AIM: Feasibility and reliability evaluation of 5G internet networks (5G IN) upon Artificial Intelligence (AI)/Machine Learning (ML), of telemonitoring...

Emergency Department Length of Stay Classification Based on Ensemble Methods and Rule Extraction.

This study employs machine learning techniques to identify factors that influence extended Emergency Department (ED) length of stay (LOS) and derives ...

Fracture risk prediction in postmenopausal women with traditional and machine learning models in a nationwide, prospective cohort study in Switzerland with validation in the UK Biobank.

Fracture prediction is essential in managing patients with osteoporosis and is an integral component of many fracture prevention guidelines. We aimed ...

[Artificial intelligence in emergency radiology: fiction or reality?].

Artificial intelligence (AI) is a rapidly advancing technology in our society. The emergency radiology is an area facing an increase of the number of ...

Unlearnable Examples Detection via Iterative Filtering

Deep neural networks are proven to be vulnerable to data poisoning attacks. Recently, a specific type of data poisoning attack known as availability...

Discovery of Green's function based on symbolic regression with physical hard constraints

The Green's function, serving as a kernel function that delineates the interaction relationships of physical quantities within a field, holds signif...

Automatic uncovering of patient primary concerns in portal messages using a fusion framework of pretrained language models.

OBJECTIVES: The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurre...

The diagnostic and triage accuracy of the GPT-3 artificial intelligence model: an observational study.

BACKGROUND: Artificial intelligence (AI) applications in health care have been effective in many areas of medicine, but they are often trained for a s...

[Research progress on the application of artificial intelligence in the early diagnosis and treatment of burn diseases].

Artificial intelligence (AI) technology is advancing rapidly, constantly presenting its application value and broad prospects in the medical field. Es...

Revolutionizing Undergraduate Learning: CourseGPT and Its Generative AI Advancements

Integrating Generative AI (GenAI) into educational contexts presents a transformative potential for enhancing learning experiences. This paper intro...