Emergency Medicine

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

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Exploring pesticide risk in autism via integrative machine learning and network toxicology.

Autism Spectrum Disorder (ASD) is a prevalent neurodevelopmental condition influenced by both geneti...

AI for fracture diagnosis in clinical practice: Four approaches to systematic AI-implementation and their impact on AI-effectiveness.

PURPOSE: Artificial Intelligence (AI) has been shown to enhance fracture-detection-accuracy, but the...

Wrist and elbow fracture detection and segmentation by artificial intelligence using point-of-care ultrasound.

PURPOSE: Distal radius (wrist) and supracondylar (elbow) fractures are common in children presenting...

Integrating surface chemistry properties and machine learning to map the toxicity landscape of superparamagnetic iron oxide nanoparticles.

The relationship between Superparamagnetic Iron Oxide Nanoparticles (SPIONs) surface chemistry and t...

Reinforcement learning using neural networks in estimating an optimal dynamic treatment regime in patients with sepsis.

OBJECTIVE: Early fluid resuscitation is crucial in the treatment of sepsis, yet the optimal dosage r...

Detecting emergencies in patient portal messages using large language models and knowledge graph-based retrieval-augmented generation.

OBJECTIVES: This study aims to develop and evaluate an approach using large language models (LLMs) a...

Predicting strength of femora with metastatic lesions from single 2D radiographic projections using convolutional neural networks.

BACKGROUND AND OBJECTIVE: Patients with metastatic bone disease are at risk of pathological femoral ...

Evaluating the National Early Warning Score (NEWS) in triage: A machine learning perspective.

BACKGROUND: The National Early Warning Score is widely used in Emergency Departments for triage, pri...

Chat-GPT in triage: Still far from surpassing human expertise - An observational study.

BACKGROUND: Triage is essential in emergency departments (EDs) to prioritize patient care based on c...

Pediatric Electrocardiogram-Based Deep Learning to Predict Secundum Atrial Septal Defects.

Secundum atrial septal defect (ASD2) detection is often delayed, with the potential for late diagnos...

Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approach.

Septic shock exhibits diverse etiologies and patient characteristics, necessitating tailored fluid m...

Early Predictive Accuracy of Machine Learning for Hemorrhagic Transformation in Acute Ischemic Stroke: Systematic Review and Meta-Analysis.

BACKGROUND: Hemorrhagic transformation (HT) is commonly detected in acute ischemic stroke (AIS) and ...

How might the rapid development of artificial intelligence affect the delivery of UK Defence healthcare?

Artificial intelligence (AI) has developed greatly and is now at the centre of technological advance...

Optimal Vasopressin Initiation in Septic Shock: The OVISS Reinforcement Learning Study.

IMPORTANCE: Norepinephrine is the first-line vasopressor for patients with septic shock. When and wh...

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