Emergency Medicine

An EEG-based hybrid machine learning approach for CT scan triage in mild traumatic brain injury.

Mild traumatic brain injury (mTBI) frequently prompts computed tomography (CT) imaging in emergency departments, despite a high proportion of negative findings. Objective, non-invasive tools that can support CT triage decisions under realistic clinical constraints are therefore needed. This study evaluates whether electroencephalography (EEG)-based biomarkers combined with temporal modeling can pr...

A review of bloodstream infections-pathogens, pathogenesis, diagnostic strategies, treatment methods-challenges and future aspects.

PURPOSE: Bloodstream infections (BSIs) remain a major cause of morbidity and mortality worldwide and continue to represent a substantial challenge to modern healthcare systems. These infections arise when pathogenic microorganisms gain access to the bloodstream, triggering systemic inflammatory responses that may progress to sepsis, septic shock, multi-organ dysfunction, and death. This review pro...

Machine learning for risk stratification of hypertensive disorders of pregnancy: Enhancing clinical efficiency in low-resource antenatal care in Tanzania.

Maternal mortality in Tanzania remains a public health crisis, with Hypertensive Disorders of Pregnancy (HDP) causing 34% of direct obstetric deaths. ...

Unraveling the adverse outcome pathways of metabolic dysfunction-associated steatotic liver disease triggered by environmental mixtures via biological knowledge-driven machine learning.

Environmental pollutant mixtures are potential risk factors for metabolic dysfunction-associated steatotic liver disease (MASLD), yet their joint effe...

Optimising pain identification in resource-limited emergency departments using transfer learning and fine-tuned language models.

OBJECTIVE: To optimise the identification of patients presenting with pain in emergency department (ED) settings with limited resources using multiple...

TRUST: an large language model-based dialogue system for trauma understanding and structured assessments.

OBJECTIVES: While large language models (LLMs) have been widely used to assist clinicians and support patients, no existing work has explored dialogue...

Prioritising parameters to minimise blood transfusions for postpartum haemorrhage: A machine learning study using national population data.

BACKGROUND: Postpartum haemorrhage (PPH) accounts for approximately 27% of maternal deaths worldwide and often requires blood transfusion, along with ...

Development and Validation of Machine Learning Models to Optimize Imaging and Referrals for Dizziness in the Emergency Department.

BACKGROUND: Dizziness and vertigo are common emergency department (ED) presentations, but only 2%-5% receive a serious diagnosis, such as stroke or tr...

Improving mortality prediction in critically ill cancer patients with a multidimensional machine learning model.

BACKGROUND: Prognostic assessment in critically ill cancer patients is challenging due to the suboptimal performance of traditional severity scores. W...

TCEP Induces Liver Injury Through Suppression of the PI3K/AKT Axis: Integrated Evidence from Epidemiology, Network Toxicology, and In Vivo Validation.

Tris(2-chloroethyl) phosphate (TCEP) is a widely used chlorinated organophosphate flame retardant, but its role in liver injury remains unclear. In th...

AI in clinical diagnostics in dermatology: applications, validation, and real-world use cases.

Artificial intelligence (AI) has moved from proof-of-concept studies in dermatology to selective, real-world clinical use, particularly in image-based...

Artificial Intelligence in skin disease therapeutics: from drug discovery to personalized treatment pathways.

Artificial intelligence (AI) comprises computational methods capable of tasks associated with human cognition, and includes specialized subfields such...

Artificial Intelligence in Disaster Triage: Enhancing Emergency Nursing Practice Through Decision Support.

Effective triage during mass casualty incidents is critical, requiring emergency nurses to make rapid decisions in high-stress, resource-limited envir...

Comparing the Efficacy Between ChatGPT 5, Grok 3, and Claude 4.5 Sonnet in Analyzing Orthopedic Trauma-Related Imaging.

OBJECTIVES: To evaluate and compare the ability of three popular open-source artificial intelligence platforms to diagnose common trauma-related fract...

Building centaur responders: is emergency management ready for artificial intelligence?

This article examines the preparedness of emergency management (EM) for addressing questions pertaining to artificial intelligence (AI), encompassing ...

From Triage to Intensive Care: A Qualitative Study of Nurses' Experiences with AI-Enabled Decision Support.

BACKGROUND: Artificial intelligence (AI)-enabled decision support systems are increasingly used in emergency departments and intensive care units to s...

Protocol-Based Management of Medication Confounders in Brain Death Determination: A Multi-Institutional Review.

BACKGROUND: Determination of death by neurologic criteria (DNC) relies on clinical examination, provided no confounding conditions affect reliability....

A Fully Automated Pipeline for Vertebral Structural Assessment From Medical Images. Application Under Metastatic Conditions.

Spinal bone metastases often lead to vertebral fractures and other skeletal events that severely affect patients' quality of life. Predicting structur...

Air pollution and hepatocellular carcinoma: Integrated network toxicology, machine learning, molecular docking and multiomics analysis.

Air pollution is closely related to the incidence and prognosis of hepatocellular carcinoma (HCC); however, the mechanisms remain elusive. Therefore, ...

Bone Mineral Density Loss After Kidney Transplantation: Metabolic Determinants, Diagnostic Approaches, and Preventive Strategies.

Mineral and bone disease (MBD) is a frequent complication after solid organ transplantation. The rapid decline in bone mineral density (BMD) during th...