Emergency Medicine

The Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI) to Support Decision Making in Emergency Departments: Model Explainability and Feature Importance Analysis.

OBJECTIVE: Evaluate the importance of specific variables contributing to a recently reported Artificial Intelligence (AI) prediction model called Sydney Triage to Admission Risk Tool with Artificial Intelligence (START-AI) to predict inpatient admission from the Emergency Department (ED). METHODS: A model explainability analysis was undertaken using single-centre ED electronic medical record data ...

Harnessing Exhaled Breath for Lung Cancer Early Detection-Results From the ExPeL Study.

Scalable, non-invasive tools are critically needed to improve early lung cancer detection and optimize primary care referral pathways. We evaluated Inflammacheck, a point-of-care device utilizing exhaled breath condensate (EBC) H2O2 and physiological parameters with machine learning for non-invasive lung cancer detection in a real-world screening population. Exhaled Hydrogen Peroxide for Early Lun...

Development and Validation of an Interpretable Machine Learning-Based Clinical Prediction Model for Short-Term Mortality in Intracerebral Hemorrhage With Thrombocytopenia: A Multicenter Study.

BACKGROUND: Intracerebral hemorrhage (ICH) with thrombocytopenia is associated with poor outcomes, but early risk prediction tools for this subgroup a...

Machine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis.

BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) is associated with high risks of mortality and disability, yet early and accurate outcome predi...

Determinants of Intentional Pharmaceutical Poisoning: A Cross-Sectional Study From a Referral Poisoning Center in Iran.

Pharmaceutical poisoning is a major health concern and a leading method of intentional self-poisoning. In Iran, broad medication access has increased ...

Ethical Risks and Governance Directions of Conversational Artificial Intelligence in Nursing Triage and Patient Education: Shared Foundations and Contextual Differences.

AIM: To provide an early-stage integrative synthesis of shared and scenario-specific ethical risks of Conversational Artificial Intelligence in nursin...

Environmental outcomes of artificial intelligence.

This paper studies the macroenvironmental consequences of AI adoption in a balanced annual panel of 30 advanced economies (EU-27, the US, UK and Japan...

Association of PM2.5 and its components with cognitive dysfunction among rural older adults: Epidemiological evidence with hypothesis-generating network toxicology analysis.

BACKGROUND: Although fine particulate matter (PM2.5) has been associated with cognitive dysfunction (CD), the roles of specific PM2.5 components and p...

Comparing the text-based diagnostic reasoning performance of emergency medicine physicians and large language models in both definitive and differential diagnoses using standardized clinical vignettes: a preliminary study.

BACKGROUND: Timely and highly accurate diagnoses by physicians play a crucial role in improving the quality and effectiveness of patient treatment out...

Classification of Inherited Retinal Diseases Using Artificial Intelligence Models for Fundus Autofluorescence and Ultrawide Retinal Images.

BACKGROUND/OBJECTIVES: Inherited retinal diseases (IRDs) are a leading cause of blindness in working-age adults. Although artificial intelligence (AI)...

Toward diagnostic precision and context-adaptive therapy in prehospital supraventricular tachycardia.

The recent scoping review by Gamberini et al. provides a comprehensive overview of the prehospital diagnosis and management of supraventricular tachyc...

Toxic effects and mechanisms of methyl 4-hydroxybenzoate in allergic rhinitis: a multi-omics and toxicology study.

PURPOSE: Exogenous chemical exposure is closely associated with allergic rhinitis (AR). Methyl 4-hydroxybenzoate (MeP), a widely used preservative, po...

Imminent opioid overdose risk prediction using classical and machine learning survival models following first recorded opioid-related diagnosis: a prospective cohort study from the All of Us research program.

Background: Identifying patients at risk of opioid overdose in healthcare settings is critical, yet evidence on predictive models and their performanc...

Integrated network toxicology and bioinformatics approaches explore potential mechanisms linking organophosphate flame retardant exposure to rheumatoid arthritis.

Organophosphate flame retardants (OPFRs) are emerging environmental pollutants characterized by high toxicity and persistence; however, their molecula...

Stress Reactions in Bone and Stress Fractures.

The term "stress fracture" encompasses a spectrum of bone injuries resulting from repetitive submaximal loading that exceeds the bone's capacity for r...

Evaluation framework for assessing the quality and safety of artificial intelligence solutions for the acute care surgeon: a narrative review from the Eastern Association for the Surgery of Trauma quality, safety, and outcomes committee.

Artificial intelligence (AI) has been developing as a field for decades. As acute care surgeons, we have been using AI in the clinical realm and in ou...

Advances in Artificial Intelligence and Machine Learning for Toxicity Prediction in Computational Toxicology: A Comprehensive Review.

Over the past three decades, artificial intelligence (AI) and machine learning (ML) have revolutionized computational toxicology, providing powerful t...

Intracranial pressure physiology, monitoring and individualized management in the acute brain injured patient.

Acute brain injury (ABI), including traumatic brain injury, ischemic and hemorrhagic stroke, is associated with high morbidity and mortality, which is...

Reach: Recognising episodes of acute complexity in health: a predictive older patient prioritisation machine learning model.

As the numbers of older people (65 +) rise globally, the pressure on acute hospitals to provide efficient and effective care while addressing resource...

Stepwise increasing input to machine learning models predicting 30-day AMI or death in emergency department chest-pain patients.

Introduction This study evaluated the performance of neural network (NN) models with stepwise increasing input for identifying acute myocardial infarc...