Emergency Medicine

Global burden of traumatic brain injury from 1990 to 2021 and projections to 2050: A GBD 2021-based study using interpretable machine learning.

Traumatic brain injury (TBI) is a major cause of disability and socioeconomic burden worldwide. Using updated Global Burden of Disease Study 2021 estimates, this study assessed global TBI burden from 1990 to 2021 and projected future patterns to 2050. Global Burden of Disease Study 2021 estimates were used to evaluate TBI incidence, prevalence, and years lived with disability (YLDs) in 204 countri...

Machine learning versus conventional grading systems for prognostication in aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis.

BACKGROUND: Accurate prognostication after aneurysmal subarachnoid hemorrhage (aSAH) remains challenging. Conventional clinical and radiological grading systems, including the World Federation of Neurosurgical Societies (WFNS), Hunt-Hess, and Fisher scales, are widely used but have limited discriminative capacity. This study aimed to systematically compare machine learning (ML)-based prognostic mo...

First-pass aspiration versus stent retriever thrombectomy: machine learning study of treatment effect heterogeneity.

BACKGROUND: Randomized trials have shown equivocal outcomes between a direct aspiration as first-pass technique (ADAPT) and stent retriever (SR) strok...

Beyond Thresholds: Can Machine Learning Improve Trauma Field Triage?

BackgroundAccurate triage of trauma patients by Emergency Medical Services (EMS) is essential for optimal outcomes and resource allocation. The 2021 N...

Treatment-Effect-Based Versus Risk-Based Targeting of Care Management Outreach in Medicaid: A Retrospective Cohort Study with Machine Learning.

Medicaid care-management programs typically allocate scarce outreach capacity to beneficiaries with the highest predicted risk of an acute event, assu...

Evaluating performance and potential clinical benefit of the Swedish On Scene Injury Severity Prediction (OSISP) model for prehospital field triage on Norwegian trauma data.

BACKGROUND: Rule-based field triage protocols used by Emergency Medicine Service (EMS) personnel for assessment and prioritization of trauma patients ...

Integrative multi-omics reveals NQO1-mediated airway epithelial injury as a key mechanism of bisphenol A-induced COPD.

Bisphenol A (BPA) is a ubiquitous environmental pollutant, but its relationship with chronic obstructive pulmonary disease (COPD) and the underlying t...

Large language models as a screening tool for the qualification of patients with Parkinson's disease for device-aided therapies.

Motor complications become increasingly prominent as Parkinson's disease (PD) progresses. Although device-aided therapies (DAT) improve symptoms contr...

Predicting KRT or Death in Critically Ill Patients with Rhabdomyolysis Using Machine Learning: A Multicenter Study.

BACKGROUND: Rhabdomyolysis is associated with outcomes ranging from muscle injury to AKI, KRT, and death. Early identification of patients at highest ...

Initial-Visit Specialty Triage in Rare Diseases Using Large Language Models: Retrospective Benchmarking Study.

BACKGROUND: Specialty triage at first contact is an overlooked step in early diagnostic pathways for rare diseases. Patients often present with overla...

Identifying treatment-responsive patient subgroups in a neutral clinical trial of Intensive blood pressure reduction in acute intracerebral hemorrhage: A post hoc explainable machine learning analysis.

The Antihypertensive Treatment of Acute Cerebral Hemorrhage (ATACH-2) trial reported no overall benefit from intensive blood pressure (BP) reduction i...

Development and Internal Validation of an Automated CT-Based Model for Early Mortality Prediction in Traumatic Brain Injury.

OBJECTIVE: Traumatic brain injury outcomes are strongly influenced by intracranial hemorrhage burden, midline shift, and secondary hypoxic injury. Alt...

Next-Generation Risk Assessment for cosmetic ingredients: a critical evaluation of published case studies.

The replacement of animal testing in cosmetic safety evaluation remains an urgent scientific and regulatory imperative. Next Generation Risk Assessmen...

Cloud-Enabled Automated CT Assessment of Pelvic Muscle Quality in Women With and Without Low-Energy Femoral Neck Fracture.

This retrospective study aimed to compare pelvic muscle parameters between female patients with and without femoral neck fractures and to evaluate the...

AI-based Synthetic CT Angiography from Non-contrast CT for Cerebrovascular Disease Screening.

BACKGROUND: CT angiography (CTA) is a key investigation in cerebrovascular disease. However, CTA is not always available and it also requires intraven...

The Future of Imaging in Heart Failure: Toward Precision Phenotyping, Integration, and Intelligence.

PURPOSE OF REVIEW: Heart failure (HF) is increasingly understood not as a single, uniformly treated diagnosis but as a heterogeneous syndrome requirin...

Non-Invasive Triage of Bladder Outlet Obstruction and Detrusor Underactivity in Men With Lower Urinary Tract Symptoms: A Narrative Review and Conceptual Framework for Depth-Aware Multimodal Transcutaneous Testing.

INTRODUCTION: Differentiating bladder outlet obstruction (BOO) from detrusor underactivity (DU) remains a major unmet need in the management of lower ...

Semantic edge detection of fractures in geological outcrops using edge aware dilated convolutional networks.

Natural fracture networks govern subsurface fluid flow, rock-mass stability, and strain accommodation in the brittle crust, yet their automated deline...

Integrating multi-source geoscience data with RF-weighted conditional variational autoencoders (CVAE) for porphyry copper prospectivity mapping: an application to the Ardestan district, Central Iran.

Porphyry Cu deposits in the Urmia-Dokhtar magmatic belt constitute important exploration targets, yet the heterogeneous and multi-scale nature of geol...

Interpretable Machine Learning Model for Predicting Sepsis and Septic Shock Among Patients with Documented Fever at Emergency Department Triage Using Patients' Historical Data.

This study aimed to develop an interpretable machine learning-based scoring system for predicting sepsis and septic shock among febrile patients at em...