Emergency Medicine

AI for predicting exacerbations in KIDs with asthma (AIRE-KIDS).

Recurrent acute care visits are a common yet preventable outcome for many children with asthma. Machine learning (ML) applied to electronic medical records (EMR) may help identify children at high risk and enable targeted referral to preventative care. We developed ML models to predict repeat asthma-related emergency department (ED) visits or hospital admissions within one year among children with...

Artificial Intelligence-Augmented Mixture Toxicology: Reframing Unresolved Risk at Camp Lejeune.

Persistent uncertainty regarding health risks associated with contaminated drinking water at Camp Lejeune reflects a broader limitation in toxicology: The difficulty of evaluating complex chemical mixtures in genetically heterogeneous populations. Historical assessments have relied on single-chemical paradigms, assumptions of dose additivity, and population-average susceptibility. These approaches...

Breaking the Limits of Low-Field MRI: Deep Learning Approaches to Image Enhancement.

Low-field magnetic resonance imaging (LF-MRI) has emerged as a transformative technology, offering portable and cost-effective solutions for medical i...

Optimising Retraining Frequency for a Paediatric Emergency Department Admission Prediction Model: Development and Temporal Validation Using Real-World Data.

OBJECTIVE: To analyse temporal performance drift and optimal retraining frequency for an ensemble machine learning model to predict inpatient admissio...

Evaluation of an AI Scribe Tool in the Emergency Department: A Single-Arm Observational Study.

BACKGROUND: Generative artificial intelligence (AI) is reshaping the way clinicians record their clinical notes. AI-scribe systems leverage generative...

Machine learning evaluation of racial and ethnic inclusion under alternative cost-based approaches to Medicare Medication Therapy Management eligibility.

BACKGROUND: The eligibility framework for the Medicare Medication Therapy Management (MTM) program has been associated with a lower likelihood of meet...

Pandemic emergency: A multi-level AI framework for analyzing psychological impact through public discourse.

Emergency management has become a widely discussed topic in recent years, particularly in relation to different types of emergencies such as climate c...

From Forecast to Action: A Deep Learning Model for Predicting Power Outages During Tropical Cyclones.

Power outages caused by tropical cyclones (TCs) pose serious risks to electric power systems and the communities they serve. Accurate, high-resolution...

Predicting depressive symptoms among Chinese college students using recurrent neural networks with longitudinal data.

College students face a higher risk of depression than their non-college peers. However, the predictors of depressive symptoms among college students ...

Mechanistic Insights into PET-MPs-Aggravated Ischemic Stroke via Integrated Computational-Experimental Approaches.

Microplastics, especially the environmentally pervasive polyethylene terephthalate microplastics (PET-MPs), are important environmental pollutants, an...

MedNet-FS: a few-shot learning framework for 3D MRI-based knee injury classification.

The development of deep learning models for 3D knee MRI analysis is critically constrained by the scarcity of large, annotated datasets. Few-shot lear...

[Artificial intelligence in diabetic retinopathy screening].

INTRODUCTION: Visual impairment and blindness continue to represent a substantial disease burden in Hungary. According to national epidemiological dat...

Deep learning-based CT-derived vertebral bone mineral density and metformin therapy: a longitudinal study in the Multi-Ethnic Study of Atherosclerosis.

OBJECTIVE: Evidence on metformin's skeletal effects remains conflicting. We emulated a target trial to evaluate associations between metformin therapy...

Identification and multicenter validation of a 4-gene plasma signature for early recognition and risk assessment in hypertensive intracerebral hemorrhage.

Hypertensive intracerebral hemorrhage (ICH) is a devastating stroke subtype with high mortality and disability, yet reliable early risk biomarkers rem...

AI-enhanced cardiac digital twins extend drug proarrhythmic risk assessment through experimental data uncertainty propagation and overdose exploration: A loperamide case study.

Drug-induced QT interval prolongation is a key biomarker of proarrhythmic risk and central to drug cardiac safety evaluation alongside in vitro assays...

Techniques and outcomes review of the endoscopic management of gastrointestinal bleeding.

INTRODUCTION: Gastrointestinal (GI) bleeding is a frequent and potentially life-threatening emergency that imposes a substantial healthcare burden wor...

Predicting facility delivery and its determinants among reproductive-age women in Ethiopia using machine learning algorithm: evidence from performance monitoring for action Ethiopia 2019 dataset.

BACKGROUND: Facility delivery attended by skilled health professionals is a critical strategy for reducing maternal and neonatal mortality. Despite su...

Environmental toxicant glyphosate induces cardiotoxicity: New insights from network toxicology, integrated machine learning, molecular modeling and multidimensional bioinformatics analysis.

Glyphosate is one of the world's most widely used herbicide, yet the causal relationship between glyphosate and cardiotoxicity remains inadequate. To ...

Ultrasound-Mediated Thrombolysis: From Mechanistic Insights to Advanced Nanoplatforms and Clinical Translation.

Although pharmacological thrombolysis and mechanical thrombectomy are standard treatments for thromboembolic diseases, they are limited by hemorrhagic...

Shift-Specific Patterns of Nursing Workloads in the Emergency Department: AI Powered Analysis.

AIM: To identify and differentiate workload patterns across shifts and to provide evidence for optimizing nursing workforce allocation in emergency de...