AIMC Topic: Emergency Service, Hospital

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Machine learning to risk stratify chest pain patients with non-diagnostic electrocardiogram in an Asian emergency department.

Annals of the Academy of Medicine, Singapore
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
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
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-...

AI-Assisted Blood Gas Interpretation: A Comparative Study With an Emergency Physician.

The American journal of emergency medicine
BACKGROUND: Blood gas interpretation is critical in emergency settings. Large language models like ChatGPT are increasingly used in clinical contexts, but their accuracy in interpreting arterial blood gases (ABGs) requires further validation.

Cost-Effectiveness Analysis of a Machine Learning-Based eHealth System to Predict and Reduce Emergency Department Visits and Unscheduled Hospitalizations of Older People Living at Home: Retrospective Study.

JMIR formative research
BACKGROUND: Dependent older people or those losing their autonomy are at risk of emergency hospitalization. Digital systems that monitor health remotely could be useful in reducing these visits by detecting worsening health conditions earlier. Howeve...

Real-life benefit of artificial intelligence-based fracture detection in a pediatric emergency department.

European radiology
OBJECTIVES: This study aimed to evaluate the performance of an artificial intelligence (AI)-based software for fracture detection in pediatric patients within a real-life clinical setting. Specifically, it sought to assess (1) the stand-alone AI perf...

Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record (EHR) data strategies.

International journal of medical informatics
OBJECTIVE: Determine the efficacy of commonly used approaches to handling missing and/or imbalanced Electronic Health Record (EHR) data on the performance of predictive models targeting risk of admission, intensive care unit (ICU) use, or prolonged l...

Communication challenges and experiences between parents and providers in South Korean paediatric emergency departments: a qualitative study to define AI-assisted communication agents.

BMJ open
OBJECTIVES: This study aimed to explore communication challenges between parents and healthcare providers in paediatric emergency departments (EDs) and to define the roles and functions of an artificial intelligence (AI)-assisted communication agent ...

Comprehensive Analysis of Heart Failure Subtypes Presenting at Emergency Department for Acute Heart Failure Management.

Journal of emergency nursing
INTRODUCTION: Despite advances in echocardiography and biomarkers, the pathophysiological complexities among heart failure categories remain incompletely understood. This study analyzed patients' characteristics across heart failure with reduced ejec...