Emergency Medicine

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 its development. However, the molecular mechanisms linking pollutant exposure to severe inflammatory lung injury remain incompletely understood. In this study, we integrated local clinical association data, bioinformatic analyses, molecular interaction...

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 to develop and externally validate a deep learning (DL) model to distinguish patients requiring surgical management from those managed non-surgically within 2 weeks of presentation using slit-lamp photographs. METHODS: A total of 1,410 slit-lamp image...

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.

Rational design of low-risk and high-efficiency β-triketone herbicide alternatives via integrative computational toxicology and NGBoost modeling.

β-Triketone herbicides (bTHs) are widely applied in crops such as maize, rice, and sorghum. However, their efficacy on some gramineous weeds is limite...

Predicting Laboratory Test Ordering in Emergency Departments Using Integrated Structured and Unstructured Electronic Health Records: Machine Learning Study.

BACKGROUND: Laboratory testing is a cornerstone of diagnostic decision-making in emergency departments (EDs), yet its overuse contributes substantiall...

Artificial intelligence for detecting subtle paediatric tibial fractures in children under three years: An analysis of YOLO architectures with convolutional Block attention.

INTRODUCTION: Subtle tibial fractures in children under three are frequently missed, particularly in cases of suspected physical abuse. Early, accurat...