AIMC Topic: Emergency Service, Hospital
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Identification of neurological text markers associated with risk of stroke.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Jun 11, 2025
BACKGROUND: Delayed or missed stroke diagnosis is associated with poor outcomes. We utilized natural language processing of notes from non-neurological emergency department (ED) encounters to identify text phrases indicating stroke presentations that...
Data Mining
Academic Medical Centers
Middle Aged
Risk Assessment
Time Factors
Emergency Service, Hospital
Stroke
Predictive Value of Tests
Natural Language Processing
Aged
Aged, 80 and over
Male
Female
Reproducibility of Results
Risk Factors
Retrospective Studies
Electronic Health Records
Humans
Prognosis
Artificial Intelligence Solutions to Improve Emergency Department Wait Times: Living Systematic Review.
The Journal of emergency medicine
Jun 10, 2025
BACKGROUND: Overcrowding and long wait times in emergency departments (EDs) remain global challenges that negatively affect patient outcomes and staff satisfaction. As an emerging technology, artificial intelligence (AI) offers the potential to optim...
Patient Triage and Guidance in Emergency Departments Using Large Language Models: Multimetric Study.
Journal of medical Internet research
May 15, 2025
BACKGROUND: Emergency departments (EDs) face significant challenges due to overcrowding, prolonged waiting times, and staff shortages, leading to increased strain on health care systems. Efficient triage systems and accurate departmental guidance are...
Identifying emergency department patients at high risk for opioid overdose using natural language processing and machine learning.
Journal of substance use and addiction treatment
May 3, 2025
INTRODUCTION: Emergency departments (ED) are potential sites for identifying and treating individuals at high risk for opioid overdose. This study used machine learning (ML)-based models to predict opioid overdose death in the 12 months after an ED v...
The role of AI in emergency department triage: An integrative systematic review.
Intensive & critical care nursing
Apr 29, 2025
BACKGROUND: Overcrowding in emergency departments (EDs) leads to delayed treatments, poor patient outcomes, and increased staff workloads. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to optimize triage.
Deep learning modelling to forecast emergency department visits using calendar, meteorological, internet search data and stock market price.
Computer methods and programs in biomedicine
Apr 24, 2025
BACKGROUND: Accurate prediction of hospital emergency department (ED) patient visits and acuity levels have potential to improve resource allocation including manpower planning and hospital bed allocation. Internet search data have been used in medic...
A cohort study of predictors of short-term nonfatal suicidal and self-harm events among individuals with mental health disorders treated in the emergency department.
Journal of psychiatric research
Apr 22, 2025
BACKGROUND: Patients presenting to emergency departments (EDs) for mental health problems have an elevated short-term risk of repeat ED visits, subsequent hospitalization, and suicide.
Machine learning to risk stratify chest pain patients with non-diagnostic electrocardiogram in an Asian emergency department.
Annals of the Academy of Medicine, Singapore
Apr 22, 2025
INTRODUCTION: Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. The myocardial-ischaemic-injury-index (MI3) algorithm is a machine learning algorithm that considers a...
Performance of the artificial intelligence-based Swiss medical assessment system versus Manchester triage system in the emergency department: A retrospective analysis.
The American journal of emergency medicine
Apr 19, 2025
BACKGROUND: The emergence of artificial intelligence (AI) offers new opportunities for applications in emergency medicine, including patient triage. This study evaluates the performance of the Swiss Medical Assessment System (SMASS), an AI-based deci...
Clinical Prediction Rules for Identifying Children With Testicular Torsion: A Multicenter Prospective Study.
Pediatric emergency care
Apr 15, 2025
OBJECTIVES: To validate clinical scores [Testicular Workup for Ischemia and Suspected Torsion (TWIST), testicular torsion (TT) score, Artificial Intelligence-based Score (AIS), Boettcher Alert Score (BALS)] when evaluating children under 18 with non-...
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