Emergency Medicine

Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED.

OBJECTIVE: The prediction of emergency department (ED) disposition at triage remains challenging. Ma...

A two-stage classification method for borehole-wall images with support vector machine.

Analyzing geological drilling hole images acquired by Axial View Panoramic Borehole Televiewer (APBT...

Current Applications and Future Impact of Machine Learning in Radiology.

Recent advances and future perspectives of machine learning techniques offer promising applications ...

Personalizing Affective Stimuli Using a Recommender Algorithm: An Example with Threatening Words for Trauma Exposed Populations.

Experimental paradigms used in affective and clinical science often use stimuli such as images, scen...

Machine Learning to Predict, Detect, and Intervene Older Adults Vulnerable for Adverse Drug Events in the Emergency Department.

Adverse drug events (ADEs) are common and have serious consequences in older adults. ED visits are o...

Evaluation of Natural Language Processing (NLP) systems to annotate drug product labeling with MedDRA terminology.

INTRODUCTION: The FDA Adverse Event Reporting System (FAERS) is a primary data source for identifyin...

Assessment of the Diagnostic Accuracy of Biparametric Magnetic Resonance Imaging for Prostate Cancer in Biopsy-Naive Men: The Biparametric MRI for Detection of Prostate Cancer (BIDOC) Study.

IMPORTANCE: Multiparametric magnetic resonance imaging (MRI) enhances detection and risk stratificat...

Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder.

OBJECTIVE: In this paper, we aim to investigate the effect of computer-aided triage system, which is...

DDC-Outlier: Preventing Medication Errors Using Unsupervised Learning.

Electronic health records have brought valuable improvements to hospital practices by integrating pa...

Detecting Opioid-Related Aberrant Behavior using Natural Language Processing.

The United States is in the midst of a prescription opioid epidemic, with the number of yearly opioi...

Open Globe Injury Patient Identification in Warfare Clinical Notes.

The aim of this study is to utilize the Defense and Veterans Eye Injury and Vision Registry clinical...

Using support vector machines on photoplethysmographic signals to discriminate between hypovolemia and euvolemia.

Identifying trauma patients at risk of imminent hemorrhagic shock is a challenging task in intraoper...

Automated detection and classification of the proximal humerus fracture by using deep learning algorithm.

Background and purpose - We aimed to evaluate the ability of artificial intelligence (a deep learnin...

Successful bystander-administered intranasal naloxone reversal of opioid overdose between two veterans: A case report.

Opioid overdose-related morbidity and mortality remain one of the most pressing public health crises...

Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic.

OBJECTIVES: Fast and accurate patient triage for the response process is a critical first step in em...

Heparan Sulfate Induces Necroptosis in Murine Cardiomyocytes: A Medical- Approach Combining Experiments and Machine Learning.

Life-threatening cardiomyopathy is a severe, but common, complication associated with severe trauma ...

Design and validation of an ontology-driven animal-free testing strategy for developmental neurotoxicity testing.

Developmental neurotoxicity entails one of the most complex areas in toxicology. Animal studies prov...

Triage, Machine Learning, Algorithms, and Becoming the Borg.

Predicting urinary tract infections in the emergency department with machine learning.

BACKGROUND: Urinary tract infection (UTI) is a common emergency department (ED) diagnosis with repor...

Automated video surveillance and machine learning: Leveraging existing infrastructure for cardiac arrest detection and emergency response activation.

Development and Validation of a Natural Language Processing Tool to Identify Patients Treated for Pneumonia across VA Emergency Departments.

BACKGROUND: Identifying pneumonia using diagnosis codes alone may be insufficient for research on cl...