Emergency Medicine

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

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Showing 442-462 of 5,216 articles
AI-assisted radiologists vs. standard double reading for rib fracture detection on CT images: A real-world clinical study.

To evaluate the diagnostic accuracy of artificial intelligence (AI) assisted radiologists and standa...

Diagnosis and detection of bone fracture in radiographic images using deep learning approaches.

INTRODUCTION: Bones are a fundamental component of human anatomy, enabling movement and support. Bon...

Attention-based deep learning models for predicting anomalous shock of wastewater treatment plants.

Quickly grasping the time-consuming water quality indicators (WQIs) such as total nitrogen (TN) and ...

Enhancing trauma triage in low-resource settings using machine learning: a performance comparison with the Kampala Trauma Score.

BACKGROUND: Traumatic injuries are a leading cause of morbidity and mortality globally, with a dispr...

A Novel Artificial Intelligence-Enhanced Digital Network for Prehospital Emergency Support: Community Intervention Study.

BACKGROUND: Efficient emergency patient transport systems, which are crucial for delivering timely m...

Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury.

Electroencephalography (EEG) is invaluable in the management of acute neurological emergencies. Char...

Enhancing smart home environments: a novel pattern recognition approach to ambient acoustic event detection and localization.

INTRODUCTION: Ambient acoustic detection and localization play a vital role in identifying events an...

Machine learning prediction of in-hospital mortality and external validation in patients with cardiogenic shock: the RESCUE score.

INTRODUCTION AND OBJECTIVES: Despite advances in mechanical circulatory support, mortality rates in ...

Death risk prediction model for patients with non-traumatic intracerebral hemorrhage.

BACKGROUND: This study aimed to assess the risk of death from non-traumatic intracerebral hemorrhage...

Ten Machine Learning Models for Predicting Preoperative and Postoperative Coagulopathy in Patients With Trauma: Multicenter Cohort Study.

BACKGROUND: Recent research has revealed the potential value of machine learning (ML) models in impr...

Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.

This study aimed to compare and evaluate the prediction accuracy and risk of bias (ROB) of post-trau...

Interpretable machine learning model for outcome prediction in patients with aneurysmatic subarachnoid hemorrhage.

BACKGROUND: Aneurysmatic subarachnoid hemorrhage (aSAH) is a critical condition associated with sign...

Machine Learning for the Early Prediction of Delayed Cerebral Ischemia in Patients With Subarachnoid Hemorrhage: Systematic Review and Meta-Analysis.

BACKGROUND: Delayed cerebral ischemia (DCI) is a primary contributor to death after subarachnoid hem...

Automatic Vertical Root Fracture Detection on Intraoral Periapical Radiographs With Artificial Intelligence-Based Image Enhancement.

BACKGROUND/AIM: To explore transfer learning (TL) techniques for enhancing vertical root fracture (V...

Predicting fall parameters from infant skull fractures using machine learning.

When infants are admitted to the hospital with skull fractures, providers must distinguish between c...

Development of a Deep Learning Tool to Support the Assessment of Thyroid Follicular Cell Hypertrophy in the Rat.

Thyroid tissue is sensitive to the effects of endocrine disrupting substances, and this represents a...

Using natural language processing to identify emergency department patients with incidental lung nodules requiring follow-up.

OBJECTIVES: For emergency department (ED) patients, lung cancer may be detected early through incide...

Factors Associated with Abusive Head Trauma in Young Children Presenting to Emergency Medical Services Using a Large Language Model.

OBJECTIVES: Abusive head trauma (AHT) is a leading cause of death in young children. Analyses of pat...

PE-GCL: Advancing pesticide ecotoxicity prediction with graph contrastive learning.

Ecotoxicity assessments, which rely on animal testing, face serious challenges, including high costs...

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