Latest AI and machine learning research in emergency medicine for healthcare professionals.
The Internet of Things (IoT) is transforming the healthcare industry by enabling real-time patient monitoring, predictive analytics and smart decision making across interconnected medical environments. It's still hard to do things like timely response in an emergency, energy efficient scheduling, secure data collection, and accurate anomaly detection, particularly in large hospital networks. This ...
INTRODUCTION: NICU sepsis evaluation balances rapid antibiotic administration for mortality reduction against unnecessary treatment. Objective risk stratification optimizes resource allocation. METHODS: We retrospectively studied 191 sepsis evaluations from 136 NICU patients. Sepsis was defined as positive blood culture or clinical sepsis with ≥5 days of antibiotics. A machine learning score (POWS...
BACKGROUND: Hand hygiene (HH) is a straightforward yet highly effective preventive measure against healthcare-associated infections; however, global c...
BACKGROUND: Simulation-based training (SBT) in neonatal resuscitation has positive impact on educational and neonatal outcomes. However, the implement...
Peripheral nerves can acquire damage through trauma, demyelinating diseases, inflammatory or immune-mediated insults, cancer, metabolic disorders, med...
In response to the challenges of insufficient precision and limited safety associated with traditional techniques in the diagnosis and treatment of co...
A continuing challenge in aerospace materials is the search for alloys that have desired functional properties to operate at higher temperatures with ...
INTRODUCTION: Cauda Equina Syndrome (CES) is a neurological emergency requiring rapid diagnosis. Traditional diagnostic methods face challenges due to...
We externally validated the performance of deep learning (DL) solution for detection of spontaneous intracerebral (ICH), intraventricular (IVH) and su...
Clinical decision-making often exhibits substantial inter-physician variability when evaluating identical patient data, limiting the reliability of co...
Accurate prediction of fire consequences is fundamental to process safety management and quantitative risk assessment in the chemical process industri...
Immersive technologies have gained increasing relevance in orthopedic surgical education; however, the scope, outcomes, and maturity of extended reali...
Diabetic kidney disease (DKD) is a major and severe complication associated with diabetes. Air pollution is not only an independent risk factor for me...
Artificial intelligence (AI) methods, including machine learning (ML), are transforming healthcare by enabling personalized interventions that integra...
BACKGROUND: Adolescents are highly vulnerable to mental health conditions, yet traditional counseling models may not fully address their evolving need...
Artificial intelligence has emerged as a promising approach for improving the detection and management of intraoperative bleeding during conventional ...
AIMS: Emergency department overcrowding, especially in cardiac units, delays care and raises mortality. Conventional triage is error-prone. We develop...
INTRODUCTION: The goal of this research is to use machine learning (ML) techniques to create a risk prediction model for postpartum stress urinary inc...
This study aimed to identify independent risk factors associated with poor wrist function recovery 6 months after internal fixation of distal radius f...