Emergency Medicine

Latest AI and machine learning research in emergency medicine for healthcare professionals.

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The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit.

BACKGROUND: Severe sepsis and septic shock are still the leading causes of death in Intensive Care U...

Chinese Emergency Event Recognition Using Conv-RDBiGRU Model.

In view of the weak generalization of traditional event recognition methods, the limitation of depen...

CT window trainable neural network for improving intracranial hemorrhage detection by combining multiple settings.

Window settings to rescale and contrast stretch raw data from radiographic images such as Computed T...

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms.

Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be learned to s...

Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach.

OBJECTIVE: Hip fractures are among the most frequently occurring fragility fractures in older adults...

Predicting hospital admission for older emergency department patients: Insights from machine learning.

BACKGROUND: Emergency departments (ED) are a portal of entry into the hospital and are uniquely posi...

Automatic Triage of 12-Lead ECGs Using Deep Convolutional Neural Networks.

BACKGROUND The correct interpretation of the ECG is pivotal for the accurate diagnosis of many cardi...

COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artificial Intelligence System.

Background Chest radiography may play an important role in triage for coronavirus disease 2019 (COVI...

Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets.

Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CX...

BPBSAM: Body part-specific burn severity assessment model.

BACKGROUND AND OBJECTIVE: Burns are a serious health problem leading to several thousand deaths annu...

Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge.

This dataset is composed of annotations of the five hemorrhage subtypes (subarachnoid, intraventricu...

Multiple Group Decision Making for Selecting Emergency Alternatives: A Novel Method Based on the LDWPA Operator and LD-MABAC.

When an emergency event occurs, it is critical to respond in the shortest possible time. Therefore, ...

Emergency department disposition prediction using a deep neural network with integrated clinical narratives and structured data.

BACKGROUND: Emergency department (ED) overcrowding has been a serious issue and demands effective cl...

Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study.

BACKGROUND: Deep learning is a novel machine learning technique that has been shown to be as effecti...

Exploring the Role of Artificial Intelligence in an Emergency and Trauma Radiology Department.

Emergency and trauma radiologists, emergency department's physicians and nurses, researchers, depart...

On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective.

The reconsolidation and extinction of aversive memories and their boundary conditions have been exte...

Revealing cytotoxic substructures in molecules using deep learning.

In drug development, late stage toxicity issues of a compound are the main cause of failure in clini...

An Integrated Approach of Machine Learning and Systems Thinking for Waiting Time Prediction in an Emergency Department.

OBJECTIVE: The objective of this study is to apply machine learning algorithms for real-time and per...

Artificial Intelligence in Dermatology: A Primer.

Artificial intelligence is becoming increasingly important in dermatology, with studies reporting ac...

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