Emergency Medicine

Machine learning detecting aggression among mood disorder patients visited in psychiatric emergency departments.

BACKGROUND: Aggression, which is highly prevalent in patients with mood disorders, has been proven valuable in detecting the progression from hypomania to manic episode, enabling a timely diagnosis and treatment. OBJECTIVE: This study aims to develop a machine learning model and explore variables detecting and distinguishing aggression among patients visited psychiatric emergency departments. METH...

Using the Electronic Health Record and Artificial Intelligence to Guide Clinical Decision-Making.

Electronic medical records (EMR) have transformed how clinical information is documented, shared, and utilized over the past 60 years, and the addition of artificial intelligence (AI) has vastly broadened EMR's capabilities. Targeted comprehensive prehabilitation services can be provided to frail patients that mitigate mortality risks. Tools for early detection of sepsis and deterioration are now ...

Automated detection and classification of dental trauma in periapical radiographs using deep learning: a study based on the Andreasen classification.

OBJECTIVE: The aim of this study was to evaluate the performance of a deep learning (DL) model in automatically identifying dental trauma types on sel...

Hitting the Bull's AI: Artificial Intelligence-derived Imaging Features and their Association with Outcomes in CT-guided Lung Biopsy, a Retrospective Study.

PURPOSE: This study aims to evaluate whether quantitative imaging features analyzed by an artificial intelligence (AI) tool are associated with succes...

Unraveling the molecular mechanisms of air pollution-induced lung adenocarcinoma through machine learning, network toxicology, and molecular docking.

OBJECTIVE: Airborne environmental contaminants are established carcinogens. This investigation elucidates the mechanistic contributions to pulmonary a...

Development and validation of machine learning models to predict PTSD at multiple time points in hospitalized trauma patients.

BACKGROUND: Posttraumatic stress disorder (PTSD) is a severe trauma-related mental disorder with high global burden. Early identification remains chal...

SERS on analyte-enriched blood for rapid, culture-free sepsis recognition and causative pathogen identification with super operational neural networks.

Sepsis remains a leading cause of morbidity and mortality, yet routine diagnostics are slow, culture-dependent, and often lack the sensitivity or spec...

Improving Acute Care Surgery with Artificial Intelligence: A Practical Review.

Artificial intelligence (AI) and machine learning are poised to transform trauma care across the entire continuum, from prehospital triage to postoper...

SOS Dental Trauma-An Artificial Intelligence Chatbot via WhatsApp for Guiding Patients After Dental Trauma.

BACKGROUND: Traumatic dental injuries (TDIs) are highly prevalent worldwide and require immediate and appropriate management to ensure favorable outco...

Pirinixic Acid Protects Against Hemorrhagic Shock by Regulating Neutrophil-Associated Key Genes.

BACKGROUND: The uncontrolled inflammatory cascade triggered by hemorrhagic shock (HS) can exacerbate tissue damage and organ dysfunction. Neutrophils,...

Can Artificial Intelligence and Machine Learning help us improve motor vehicle crash trauma triage?

Understanding and Addressing Bias in Artificial Intelligence Systems: A Primer for the Emergency Medicine Physician.

Artificial intelligence (AI) tools and technologies are increasingly being integrated into emergency medicine (EM) practice, not only offering potenti...

Early Insights Among Emergency Medicine Physicians on Artificial Intelligence: A National, Convenience-sample Survey of the American College of Emergency Physicians.

OBJECTIVES: This study aimed to assess the current utilization of artificial intelligence (AI) tools among emergency physicians, their attitudes towar...

Micro- and nanoplastics (MNPs) in liquid food: From occurrence, health risks and migration mechanisms to AI-enabled analytical and circular solutions.

Microplastics (MPs, 1 μm-5 mm) and nanoplastics (NPs, <1 μm) are routinely detected in wide array of liquid food, including drinking water, milk, beve...

Elevated Retinal Neovascularization on Widefield Optical Coherence Tomography Angiography Predicts Complications in High-Risk Proliferative Diabetic Retinopathy.

PURPOSE: To determine whether retinal neovascularization (RNV) metrics derived from single-shot widefield swept-source OCT angiography (SS-OCTA) predi...

Generation of explainable QSAR models for predicting chemical-induced urinary tract toxicity.

Chemical-induced urinary tract toxicity, particularly in the bladder and ureters, remains undercharacterized relative to nephrotoxicity. We present an...

Prediction of independent ambulation at hospital discharge in patients with proximal femoral fractures based on preoperative clinical information: A retrospective study using extreme gradient boosting and SHapley additive explanations.

BACKGROUND: Independent ambulation at hospital discharge is a critical determinant of discharge destination and caregiving burden in older adults with...

A Machine Learning Strategy to Predict the Number of High-Acuity Children Who Leave Without Being Seen From the Emergency Department.

OBJECTIVES: The purpose of this study was to create an operationally useful machine learning model that predicts the number of high-acuity left withou...

Ensemble machine learning for early mortality risk stratification in septic orthopedic trauma: an international cohort study.

BACKGROUND: Sepsis represents a life-threatening complication in severe orthopedic trauma, significantly increasing short-term mortality risk. Despite...

Ethical challenges and opportunities for integrating predictive analytics in community-based overdose prevention.

As predictive analytics become more widely integrated into local public health responses to the United States overdose epidemic, community-based subst...