AIMC Topic: Emergency Service, Hospital
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Prediction of Adolescent Suicide Attempt by Integrating Clinical, Neurocognitive and Geocoded Neighborhood Environment Data.
Schizophrenia bulletin
Jul 7, 2025
BACKGROUND AND HYPOTHESIS: Suicide attempt is a complex behavior influenced by a combination of factors including clinical, neurocognitive, and environmental. We aimed to leverage multimodal data collected during pre/early adolescence in research set...
Clinical Decision Support for Septic Shock in the Emergency Department: A Cluster Randomized Trial.
Pediatrics
Jul 1, 2025
BACKGROUND AND OBJECTIVES: Delays in septic shock diagnosis cause preventable mortality in children. Evidence is limited around early recognition strategies. The hypothesis was that clinical decision support (CDS) based on machine-learning predictive...
Re-engineering the clinical approach to suspected cardiac chest pain assessment in the emergency department by expediting research evidence to practice using artificial intelligence. (RAPIDx AI)-a cluster randomized study design.
American heart journal
Jul 1, 2025
BACKGROUND: Clinical work-up for suspected cardiac chest pain is resource intensive. Despite expectations, high-sensitivity cardiac troponin assays have not made decision making easier. The impact of recently validated rapid triage protocols includin...
A Meta-Analysis of the Diagnostic Test Accuracy of Artificial Intelligence for Predicting Emergency Department Revisits.
Journal of medical systems
Jun 16, 2025
The revisit of the emergency department (ED) is a key indicator of emergency care quality. Various strategies have been proposed to reduce ED revisits, including the use of artificial intelligence (AI) models for prediction. However, AI model perform...
Early Prediction of Septic Shock in Emergency Department Using Serum Metabolites.
Journal of the American Society for Mass Spectrometry
Jun 4, 2025
Early recognition of septic shock is crucial for improving clinical management and patient outcomes, especially in the emergency department (ED). This study conducted serum metabolomic profiling on ED patients diagnosed with septic shock (n = 32) and...
Quantifying Healthcare Provider Perceptions of a Novel Deep Learning Algorithm to Predict Sepsis: Electronic Survey.
Critical care explorations
Jun 1, 2025
IMPORTANCE: Sepsis is a major cause of morbidity and mortality, with early intervention shown to improve outcomes. Predictive modeling and artificial intelligence (AI) can aid in early sepsis recognition, but there remains a gap between algorithm dev...
Detection of emergency department patients at risk of dementia through artificial intelligence.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Jun 1, 2025
INTRODUCTION: The study aimed to develop and validate the Emergency Department Dementia Algorithm (EDDA) to detect dementia among older adults (65+) and support clinical decision-making in the emergency department (ED).
Clinician Suicide Risk Assessment for Prediction of Suicide Attempt in a Large Health Care System.
JAMA psychiatry
Jun 1, 2025
IMPORTANCE: Clinical practice guidelines recommend suicide risk screening and assessment across behavioral health settings. The predictive accuracy of real-world clinician assessments for stratifying patients by risk of future suicidal behavior, howe...
Evaluating the National Early Warning Score (NEWS) in triage: A machine learning perspective.
International emergency nursing
Jun 1, 2025
BACKGROUND: The National Early Warning Score is widely used in Emergency Departments for triage, primarily to predict mortality. However, its effectiveness in assessing additional clinical outcomes relevant to triage, such as patient urgency and seve...
Chat-GPT in triage: Still far from surpassing human expertise - An observational study.
The American journal of emergency medicine
Jun 1, 2025
BACKGROUND: Triage is essential in emergency departments (EDs) to prioritize patient care based on clinical urgency. Recent investigations have explored the role of large language models (LLMs) in triage, but their effectiveness compared to human tri...
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