Emergency Medicine

Latest AI and machine learning research in emergency medicine for healthcare professionals.

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Showing 211-231 of 5,216 articles
HealthiVert-GAN: A Novel Framework of Pseudo-Healthy Vertebral Image Synthesis for Interpretable Compression Fracture Grading.

Osteoporotic vertebral compression fractures (OVCFs) are prevalent in the elderly population, typica...

Using Traditional and Deep Machine Learning to Predict Emergency Room Triage Levels.

Accurate triage in emergency rooms is crucial for efficient patient care and resource allocation. We...

Performance of multimodal prediction models for intracerebral hemorrhage outcomes using real-world data.

BACKGROUND: We aimed to develop and validate multimodal models integrating computed tomography (CT) ...

A Machine Learning Method for Predicting Acute Kidney Injury in Patients with Intracranial Hemorrhage.

Intracranial hemorrhage (ICH) is a critical and urgent condition in clinical practice. Recent resear...

Systematic review on the impact of deep learning-driven worklist triage on radiology workflow and clinical outcomes.

OBJECTIVES: To perform a systematic review on the impact of deep learning (DL)-based triage for redu...

Data-driven identification of urgent surgical procedures for use in trauma outcomes measurement.

BACKGROUND: No standardized list of urgent-trauma-surgery exists for analysis in injury studies. If ...

Exploring Hidden Dangers: Predicting Mycotoxin-like Toxicity and Mapping Toxicological Networks in Hepatocellular Carcinoma.

Mycotoxins are potent triggers of hepatocellular carcinoma (HCC) due to their intricate interplay wi...

Influences on trust in the use of AI-based triage-an interview study with primary healthcare professionals and patients in Sweden.

INTRODUCTION: Artificial intelligence (AI) has the potential to improve the quality and efficiency o...

Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images.

Intracerebral haemorrhage (ICH) is a common form of stroke that affects millions of people worldwide...

Applying Machine Learning to the ANZELA-QI Database to Predict Adverse Outcomes for Patients Undergoing Emergency Laparotomy.

BACKGROUND: Emergency laparotomy is associated with high rates of morbidity and mortality. Accurate,...

Development of a deep-learning algorithm for etiological classification of subarachnoid hemorrhage using non-contrast CT scans.

OBJECTIVES: This study aims to develop a deep learning algorithm for differentiating aneurysmal suba...

Dynamic Predictive Models of Cardiogenic Shock in STEMI: Focus on Interventional and Critical Care Phases.

: While early risk stratification in STEMI is essential, the threat of cardiogenic shock (CS) persis...

Research on Machine Learning Models Based on Cranial CT Scan for Assessing Prognosis of Emergency Brain Injury.

BACKGROUND: To evaluate the prognosis of patients with traumatic brain injury according to the Compu...

Impact of test set composition on AI performance in pediatric wrist fracture detection in X-rays.

OBJECTIVES: To evaluate how different test set sampling strategies-random selection and balanced sam...

A deep learning-based approach to automated rib fracture detection and CWIS classification.

PURPOSE: Trauma-induced rib fractures are a common injury. The number and characteristics of these f...

Patient Triage and Guidance in Emergency Departments Using Large Language Models: Multimetric Study.

BACKGROUND: Emergency departments (EDs) face significant challenges due to overcrowding, prolonged w...

The impact of different task contexts on emergency responders' trust and usage intention of artificial intelligence.

Proper use of artificial intelligence (AI) can significantly enhance emergency responders' performan...

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