Emergency Medicine

Interpretable machine learning model for predicting hemorrhage following ultrasound-guided percutaneous renal biopsy: a multicenter study.

OBJECTIVE: This study aims to develop and validate an interpretable machine learning (ML) model for predicting post-procedural hemorrhage (PH) after ultrasound-guided percutaneous renal biopsy (PRB), aiding perioperative management. MATERIALS AND METHODS: A retrospective analysis was conducted using data from 664 patients to develop and internally validate the predictive model. Key ultrasound para...

Multi-scale modeling of habitat suitability and human-induced risk for the critically endangered white-rumped vulture (Gyps bengalensis) in South and Southeast Asia.

The White-rumped Vulture is a critically endangered species, facing rapid population declines across South and Southeast Asia due to habitat loss, anthropogenic disturbances, and toxicological pressures. This study uses species distribution modeling (SDM) to assess the current and future habitat suitability for the White-rumped Vulture across its geographic range in the region. We employed MaxEnt,...

Machine-learning algorithms identifies sTREM1 has a key biomarker for outcome prediction in critically ill.

INTRODUCTION: Prognostic assessment in critically ill patients traditionally relies on severity scores or single biomarkers, each with limited ability...

Predicting low birth weight in Bangladesh using interpretable machine learning models.

BACKGROUND: Low birth weight (LBW) remains a leading cause of neonatal mortality and long-term morbidity in low- and middle-income countries. This stu...

Genome-Wide Association Study and Fine Mapping Uncover Key Genetic Loci and Credible Genes of Intracranial Aneurysm and Aneurysmal Subarachnoid Hemorrhage.

BACKGROUND: Intracranial aneurysm (IA) is a common neurovascular disorder; rupture causes aneurysmal subarachnoid hemorrhage with high mortality. Desp...

Targeted screening for heart failure in primary care: evidence, challenges and a pathway-first approach to implementation.

INTRODUCTION: Heart failure remains a leading cause of morbidity and mortality, yet diagnosis continues to occur late in routine practice, frequently ...

Angioarchitectural features of hemorrhagic presentation of brain arteriovenous malformations: A multivariate and explainable machine learning study.

PURPOSE: Brain arteriovenous malformations (BAVM) pose a significant rupture risk, leading to morbidity and mortality. Identifying features associated...

Gene-Environment Interactions in Predicting Self-Harm: A Machine Learning Approach Using Explainable Artificial Intelligence.

BACKGROUND: Self-harm is a critical public health concern; nevertheless, the complex interplay between genetic predispositions and environmental facto...

Multimodal GPT-5 for Predicting Poor Functional Outcomes After Intracerebral Hemorrhage in the Emergency Department: Validation Study.

BACKGROUND: In the emergency department, rapid prognostic assessment of patients with intracerebral hemorrhage (ICH) is essential for guiding early ma...

Reducing Mis-triage in Emergency Departments (RemEDy): Protocol for Improving Triage Accuracy Through Real-time Evaluation and Artificial Intelligence.

BACKGROUND: Mis-triage represents a global concern, with reported rates ranging from 15% to 33%. Understanding its causes and contributing factors is ...

Rerouting Eye Care: How AI and Telemedicine Are Reshaping Ophthalmology Patient Journeys in the United Kingdom and Germany.

Artificial intelligence (AI) as a medical device is now progressively entering routine ophthalmic care, yet its impact is still mostly evaluated in te...

Feasibility study of an AI-powered mobile app to support cutaneous leishmaniasis diagnosis in the Brazilian Amazon.

BACKGROUND: Cutaneous leishmaniasis (CL) remains a major public health challenge, especially in Brazil's Amazon, where environmental and economic pres...

Diagnostic value of integrated albumin-to-leukocyte ratio risk modeling stratification and machine learning approaches for prediction of spontaneous bacterial peritonitis in cirrhotic patients.

BACKGROUND: Spontaneous bacterial peritonitis (SBP) remains a life-threatening complication of liver cirrhosis, requiring accurate and rapid predictio...

Next generation risk assessment and new approach methodologies for sustainable management of heavy metal contaminated environments: A critical review.

Environmental stability forms the foundation for sustaining healthy ecosystems and protecting human life, but the relentless accumulation of persisten...

Real-world implementation of clinical decision support systems in emergency departments: a scoping review protocol.

OBJECTIVE: This scoping review will address one area of artificial intelligence (AI) applications: clinical decision support systems (CDSSs) implement...

Coconstructing CHAMP, an Artificial Intelligence Chatbot for Pediatric Infectious Symptoms Management: Protocol for a Multiphase Participatory Study.

BACKGROUND: Acute infectious symptoms are a leading cause of pediatric emergency department visits in Canada, many of which are low acuity and could b...

Survival prediction modeling for 1-year mortality in patients with ST elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI).

BACKGROUND: ST elevation myocardial infarction (STEMI) is a life-threatening condition, and is associated with significant mortality, especially in pa...

Elucidating the pathogenic mechanism underlying plasticizer-induced chronic kidney disease via integrated network toxicology and machine learning.

Plasticizers are ubiquitous plastic additives that have been linked to multiple adverse health outcomes and elevated risks of chronic kidney disease (...

Multicentre prospective cohort study to develop and validate a machine learning-based model for predicting 6-month all-cause mortality in elderly patients with advanced chronic obstructive pulmonary disease in China: study protocol.

INTRODUCTION: Chronic obstructive pulmonary disease (COPD) has an unpredictable clinical course, causing difficulties in short-term mortality predicti...

Artificial Intelligence in Neuroradiology: A Review of FDA-regulated Algorithms.

Artificial intelligence (AI) is increasingly integrated into neuroradiology practice, with a growing number of FDA-cleared algorithms now supporting t...