Emergency Medicine

Consensus Development of a Modern Ontology of Emergency Department Presenting Problems-The Hierarchical Presenting Problem Ontology (HaPPy).

OBJECTIVE: Numerous attempts have been made to create a standardized "presenting problem" or "chief complaint" list to characterize the nature of an emergency department visit. Previous attempts have failed to gain widespread adoption as they were not freely shareable or did not contain the right level of specificity, structure, and clinical relevance to gain acceptance by the larger emergency med...

Artificial Intelligence in Musculoskeletal Imaging: Current Status and Future Directions.

The objective of this article is to show how artificial intelligence (AI) has impacted different components of the imaging value chain thus far as well as to describe its potential future uses. The use of AI has the potential to greatly enhance every component of the imaging value chain. From assessing the appropriateness of imaging orders to helping predict patients at risk for fracture, AI can...

SOCIETY FOR ENDOCRINOLOGY EMERGENCY ENDOCRINE GUIDANCE: Emergency management of acute hypocalcaemia in adult patients.

In this addendum to the above paper, the Society for Endocrinology Clinical Committee and the original authors provide additional advice on the dose e...

Automated brain histology classification using machine learning.

Brain and breast tumors cause significant morbidity and mortality worldwide. Accurate and expedient histological diagnosis of patients' tumor specimen...

Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds.

P-glycoprotein (P-gp) is a transmembrane protein that actively transports a wide variety of chemically diverse compounds out of the cell. It is highly...

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia.

Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG featu...

Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability.

In the terrestrial ecosystems, perennial challenges of increased frequency and intensity of wildfires are exacerbated by climate change and unplanned ...

Machine learning prediction of nanoparticle in vitro toxicity: A comparative study of classifiers and ensemble-classifiers using the Copeland Index.

Nano-Particles (NPs) are well established as important components across a broad range of products from cosmetics to electronics. Their utilization is...

A machine learning approach for predicting urine output after fluid administration.

BACKGROUND AND OBJECTIVE: To develop a machine learning model to predict urine output (UO) in sepsis patients after fluid resuscitation.

Image Thresholding Improves 3-Dimensional Convolutional Neural Network Diagnosis of Different Acute Brain Hemorrhages on Computed Tomography Scans.

Intracranial hemorrhage is a medical emergency that requires urgent diagnosis and immediate treatment to improve patient outcome. Machine learning alg...

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 younger people, early intervention and prompt diagnosi...

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

BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) is a devastating disease with high mortality rate. This study aimed to predict hematoma expansi...

Classification of Patients with Coronary Microvascular Dysfunction.

While coronary microvascular dysfunction (CMD) is a major cause of ischemia, it is very challenging to diagnose due to lack of CMD-specific screening ...

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 recurrent neural network (CNN-RNN) for the detection...

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 complications, using the non-adhesive liquid embolic mat...

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 techniques are useful for analyzing narratives, but ...

Machine learning for phenotyping opioid overdose events.

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

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 (SAH), there are no reliable biomarkers to predict...

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, quality of life, health outcomes and life expecta...

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 conventional statistical approaches in predicting trauma ...