Emergency Medicine

Mechanism-Based Multitarget Modeling for Pathway-Level Prediction of PI3K/Akt Signaling Perturbation Induced by Liquid Crystal Monomers.

Liquid crystal monomers (LCMs) are emerging contaminants whose system-level toxicity mechanisms remain poorly understood. Here, we developed a pathway-centric multitarget framework to characterize coordinated toxicological perturbations at the signaling network level. KEGG enrichment identified the PI3K/Akt pathway as a key mechanistic axis, and a minimal set of 19 proteins covering upstream recep...

The Utility of Large Language Models to Assist With Emergency Triage Decisions Within Otolaryngology.

OBJECTIVE: To determine whether contemporary large language models can match clinician performance in evaluating the urgency of emergency otolaryngology referrals. STUDY DESIGN: Blinded cross-sectional diagnostic reasoning study. SETTING: Simulated emergency referral environment modeled on tertiary care otolaryngology practice. METHODS: Thirty emergency referral scenarios spanning the spectrum of ...

Artificial Intelligence-Enabled Serial Electrocardiograms for Prediction of All-Cause Mortality in Secondary Care Settings.

BACKGROUND: Prognostic assessment in secondary care settings remains challenging and may influence clinical decision-making and follow-up. Artificial ...

Integrated toxicological analysis links air pollution-associated acute respiratory distress syndrome signatures to benzo[a]pyrene-induced acute lung injury.

Acute respiratory distress syndrome (ARDS) is associated with high mortality, and increasing evidence suggests that air pollution may contribute to it...

Beyond Diagnosis: AI-Based Surgical Risk Assessment in Acanthamoeba Keratitis with Real-World Validation.

PURPOSE: Acanthamoeba keratitis (AK) is a sight-threatening infection in which delayed diagnosis can lead to surgical intervention. This study aimed t...

Heterogeneity of Clazosentan Benefit after Aneurysmal Subarachnoid Hemorrhage Demonstrated in an Externally Validated Causal Policy Tree.

BACKGROUND: Clazosentan reduces angiographic vasospasm after aneurysmal subarachnoid hemorrhage (aSAH), but functional benefit may vary across patient...

Computer-aided diagnosis system for thoracic computed tomography of rib fractures in older emergency patients: A preliminary study.

The peculiarities of older individuals related to osteoporosis and hyperostosis may lead to a higher rate of misdiagnosis of rib fractures on computed...

The voice of decompensation: an explainable machine learning approach to assessing heart failure severity.

AIMS: Heart failure (HF) is characterized by high morbidity and frequent hospital readmissions, highlighting the need for scalable out-of-hospital mon...

Deep learning-based natural language processing for critical care identification in pediatric emergency department.

BACKGROUND: The initial assessment of pediatric emergency patients is challenging due to diverse clinical presentations and the limited applicability ...

Hybrid deep learning ensemble model for detecting small to medium rotator cuff tears from shoulder radiographs.

BACKGROUND: Rotator cuff tears (RCTs) represent a common orthopedic condition, the diagnosis of which often requires advanced imaging techniques. Nota...

ToxiSpecies: Task-Aware Meta-Learning for Cross-Species Modeling of Acute Chemical Toxicity under Distribution Shift.

Reliable modeling of chemical toxicity across species poses a fundamental challenge for molecular property prediction, as toxicity data sets are inher...

Machine learning to identify novel bleeding and residual thromboembolic risks in patients on anticoagulation.

Balancing thromboembolic prevention against bleeding risk remains a key challenge during oral anticoagulant (OAC) therapy. CHA₂DS₂-VASc cannot predict...

The AI implementation gap in trauma radiography: standalone versus discretionary AI-integrated fracture detection.

OBJECTIVE: In emergency trauma care, artificial intelligence (AI) may aid fracture detection on radiographs, potentially reducing radiologists' worklo...

Hypericum perforatum L.: a legacy of herbal medicine in light of modern scientific evidence.

ETHNOPHARMACOLOGICAL RELEVANCE: Hypericum perforatum L. has been used for centuries in traditional medicine, with core therapeutic applications includ...

Global performance of predictive models for dengue severity, hospitalization and mortality: a systematic review and meta-analysis of 146 studies.

OBJECTIVES: Predictive models are increasingly used to support the clinical management of dengue, but their performance varies widely across settings....

Aligning neonatal sepsis triage diagnostics with WHO target product profiles in low- and middle-income countries.

Neonatal sepsis is a leading cause of infant morbidity and mortality in low- and middle-income countries (LMICs), yet despite this, progress in the de...

Towards clinically interpretable machine learning in emergency surgery: feature importance and insights across clinical time points in abdominal pain cases.

PURPOSE: Machine learning (ML) may support decision-making for acute abdominal pain (AAP), but limited interpretability hinders adoption. We evaluated...

Interpretable Machine Learning in Heavy Metal-Associated Cardiovascular and Metabolic Disease: A Review of Current Methodologies, Toxicological Insights, and Clinical Implications.

Heavy metals, including lead (Pb), cadmium (Cd), arsenic (As), and mercury (Hg), are pervasive environmental toxicants increasingly recognized as nont...

Optimizing Extracorporeal Cardiopulmonary Resuscitation Candidate Selection in out-of-Hospital Cardiac Arrest: A Machine-Learning Individualized Treatment Effect Approach Versus Rule-Based Criteria.

BACKGROUND: Extracorporeal cardiopulmonary resuscitation (ECPR) has demonstrated survival benefit in selected patients with out-of-hospital cardiac ar...

Humans Versus Machine: Machine Learning-Based Individualized Treatment Effect Modeling for Extracorporeal Cardiopulmonary Resuscitation in Out-of-Hospital Cardiac Arrest.