Emergency Medicine

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...

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...

Hospital crowdedness evaluation and in-hospital resource allocation based on image recognition technology.

How to allocate the existing medical resources reasonably, alleviate hospital congestion and improve...

Urban Flooding Prediction Method Based on the Combination of LSTM Neural Network and Numerical Model.

At present, urban flood risk analysis and forecasting and early warning mainly use numerical models ...

[Computer-assisted procedures in orthopedics and trauma surgery-Where do we stand?].

Computer-assisted procedures are becoming increasingly more relevant in orthopedics and trauma surge...

Surgeons' perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: results from an international survey.

BACKGROUND: Artificial intelligence (AI) is gaining traction in medicine and surgery. AI-based appli...

A deep learning model for prognosis prediction after intracranial hemorrhage.

BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) is a common life-threatening condition that mu...

Assessing machine learning approaches for predicting failures of investigational drug candidates during clinical trials.

One of the major challenges in drug development is having acceptable levels of efficacy and safety t...