Emergency Medicine

Using artificial intelligence-based software for an unbiased discrimination of immune cell subtypes in the fracture hematoma and bone marrow of non-osteoporotic and osteoporotic mice.

It is well established that the early inflammatory response following fracture is essential for initiating subsequent bone regeneration. An imbalance in inflammation, whether within the innate or adaptive immune response, can result in impaired fracture healing. In our previous studies, we demonstrated that, for example, mice with ovariectomy-induced osteoporosis exhibit altered immune cell popula...

Predicting 30-day survival after in-hospital cardiac arrest: a nationwide cohort study using machine learning and SHAP analysis.

OBJECTIVE: In-hospital cardiac arrest (IHCA) presents a critical challenge with low survival rates and limited prediction tools. Despite advances in resuscitation, predicting 30-day survival remains difficult, and current methods lack interpretability for timely decision-making. This study developed a machine learning (ML) model to predict 30-day survival after IHCA, using peri-arrest variables av...

Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression.

Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (...

Leveraging ChatGPT for improved decision-making in interfacility transfers for plastic surgery emergencies based on United States guidelines.

Plastic surgery emergencies-such as burns, facial trauma, and upper extremity injuries-often require timely specialist consultation. Yet, interfacilit...

A Novel Natural Language Processing Model for Triaging Head and Neck Patient Appointments.

OBJECTIVE: Inaccurate patient triage contributes to suboptimal clinical capacity management and delays in patient care, which in cancer patients may s...

Electrosurgical-laceration and stabilization of two PASCAL devices using artificial intelligence-based procedural planning: a case report.

BACKGROUND: Mitral transcatheter edge-to-edge repair (M-TEER) has been established as a treatment for severe mitral regurgitation (MR). However, recur...

Vision transformer and deep learning based weighted ensemble model for automated spine fracture type identification with GAN generated CT images.

The most common causes of spine fractures, or vertebral column fractures (VCF), are traumas like falls, injuries from sports, or accidents. CT scans a...

Machine learning for predicting medical outcomes associated with acute lithium poisoning.

The use of machine learning algorithms and artificial intelligence in medicine has attracted significant interest due to its ability to aid in predict...

Assessing artificial intelligence-generated patient discharge information for the emergency department: a pilot study.

BACKGROUND: Effective patient discharge information (PDI) in emergency departments (EDs) is vital and often more crucial than the diagnosis itself. Pa...

Deep learning modelling to forecast emergency department visits using calendar, meteorological, internet search data and stock market price.

BACKGROUND: Accurate prediction of hospital emergency department (ED) patient visits and acuity levels have potential to improve resource allocation i...

Ambulance route optimization in a mobile ambulance dispatch system using deep neural network (DNN).

The ambulance dispatch system plays a crucial role in emergency medical care by ensuring efficient communication, reducing response times, and ultimat...

Identification of pivotal genes and regulatory networks associated with SAH based on multi-omics analysis and machine learning.

Subarachnoid hemorrhage (SAH) is a disease with high mortality and morbidity, and its pathophysiology is complex but poorly understood. To investigate...

Identification of immune-related biomarkers and immune infiltrations of intracranial aneurysm with subarachnoid hemorrhage by machine-learning strategies.

Background: Subarachnoid hemorrhage (SAH) risk increases with intracranial aneurysms (IA), but their relationship remains unclear. Methods: We explore...

Advancing emergency upper extremity care: A pilot study of ChatGPT's potential role in diagnosing and managing hand and wrist trauma.

PURPOSE: Hand and wrist trauma is a frequent cause of emergency room (ER) visits. However, hospitals often lack immediate hand specialist coverage. Th...

Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care.

Primary care consultations provide an opportunity for patients and clinicians to assess asthma attack risk. Using a data-driven risk prediction tool w...

The association of trimethylamine N-oxide with diabetic retinopathy Pathology: Insights from network toxicology and molecular docking analysis.

Trimethylamine N-oxide (TMAO), a gut microbiota-derived metabolite, has emerged as a potential contributor to diabetic retinopathy (DR) progression. H...

A cohort study of predictors of short-term nonfatal suicidal and self-harm events among individuals with mental health disorders treated in the emergency department.

BACKGROUND: Patients presenting to emergency departments (EDs) for mental health problems have an elevated short-term risk of repeat ED visits, subseq...

Using deep learning for estimation of time-since-injury in pediatric accidental fractures.

BACKGROUND: Estimating time-since-injury of healing fractures is imprecise, encompassing excessively wide timeframes. Most injured children are evalua...

Using the Geriatric Emergency Perioperative Risk Index Derived From Artificial Intelligence Algorithms to Predict Outcomes of Geriatric Emergency General Surgery.

INTRODUCTION: The objective of this study was to employ artificial intelligence (AI) technology for the development of a model that can accurately for...

National Cost Savings From Use of Artificial Intelligence Guided Echocardiography in the Assessment of Intermediate-Risk Patients With Syncope in the Emergency Department.

OBJECTIVES: Our primary objective was to estimate the realistic impact of an artificial intelligence (AI)-based trans-thoracic echocardiogram (TTE)-fi...