Emergency Medicine

Deep Belief CNN Feature Representation Based Content Based Image Retrieval for Medical Images.

Avascular Necrosis (AN) is a cause of muscular-skeletal disability. As it is common amongst the youn...

Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine.

BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) is a devastating disease with high mortality ...

Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network.

OBJECTIVES: To evaluate the performance of a novel three-dimensional (3D) joint convolutional and re...

Embolizing intracranial arteriovenous malformations with Onyx: experience at a single center with 250 patients.

The aim of this study is to evaluate the embolization techniques, as well as the effects and compli...

Analysis of Unstructured Text-Based Data Using Machine Learning Techniques: The Case of Pediatric Emergency Department Records in Nicaragua.

Free-text information is still widely used in emergency department (ED) records. Machine learning te...

Machine learning for phenotyping opioid overdose events.

OBJECTIVE: To develop machine learning models for classifying the severity of opioid overdose events...

A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop.

Imaging research laboratories are rapidly creating machine learning systems that achieve expert huma...

Machine Learning Analysis of Matricellular Proteins and Clinical Variables for Early Prediction of Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage.

Although delayed cerebral ischemia (DCI) is a well-known complication after subarachnoid hemorrhage ...

Moonstone: a novel natural language processing system for inferring social risk from clinical narratives.

BACKGROUND: Social risk factors are important dimensions of health and are linked to access to care,...

Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma.

OBJECTIVE: Machine learning techniques have demonstrated superior discrimination compared to convent...

Developing neural network models for early detection of cardiac arrest in emergency department.

BACKGROUND: Automated surveillance for cardiac arrests would be useful in overcrowded emergency depa...

Plaque components segmentation in carotid artery on simultaneous non-contrast angiography and intraplaque hemorrhage imaging using machine learning.

PURPOSE: This study sought to determine the feasibility of using Simultaneous Non-contrast Angiograp...

Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs.

OBJECTIVE: To identify the feasibility of using a deep convolutional neural network (DCNN) for the d...

Quantification of apixaban in human plasma using ultra performance liquid chromatography coupled with tandem mass spectrometry.

Apixaban, an inhibitor of direct factor Xa, is used for the treatment of venous thromboembolic event...

Chief complaint classification with recurrent neural networks.

Syndromic surveillance detects and monitors individual and population health indicators through sour...

Mass Surveilance of -Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection.

The nematode is often used as an alternative animal model due to several advantages such as morphol...

Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity.

In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first US legisl...

Machine Learning for Prediction of Posttraumatic Stress and Resilience Following Trauma: An Overview of Basic Concepts and Recent Advances.

Posttraumatic stress responses are characterized by a heterogeneity in clinical appearance and etiol...

[Ethics and artificial intelligence in health: The emergency of an integrated approach].

Use of artificial neural networks to identify the predictive factors of extracorporeal shock wave therapy treating patients with chronic plantar fasciitis.

The purpose of our study is to identify the predictive factors for a minimum clinically successful t...

Hybrid Degradation Equipment Remaining Useful Life Prediction Oriented Parallel Simulation considering Model Soft Switch.

Equipment parallel simulation is an emerging simulation technology in recent years, and equipment re...