AIMC Topic: Triage

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Machine learning to improve predictive performance of prehospital early warning scores.

Scientific reports
Early warning scores are used to assess acute patients' risk of being in a critical situation, allowing for early appropriate treatment, avoiding critical outcomes. The early warning scores use changes in vital signs to provide an assessment, however...

Developing interpretable machine learning models to predict length of stay and disposition decision for adult patients in emergency departments.

BMJ health & care informatics
OBJECTIVE: Machine learning (ML) models have emerged as tools to predict length of stay (LOS) and disposition decision (DD) in emergency departments (EDs) to combat overcrowding. However, site-specific ML models are not transferable to different site...

A Machine Learning Trauma Triage Model for Critical Care Transport.

JAMA network open
IMPORTANCE: Under austere prehospital conditions, rapid classification of injured patients for intervention or transport is essential for providing lifesaving care. Discerning which patients need care most urgently further allows for optimal allocati...

Patient Triage and Guidance in Emergency Departments Using Large Language Models: Multimetric Study.

Journal of medical Internet research
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...

Validation of an Artificial Intelligence-Powered Virtual Assistant for Emergency Triage in Neurology.

The neurologist
OBJECTIVES: Neurological emergencies pose significant challenges in medical care in resource-limited countries. Artificial intelligence (AI), particularly health chatbots, offers a promising solution. Rigorous validation is required to ensure safety ...

A Novel Natural Language Processing Model for Triaging Head and Neck Patient Appointments.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: Inaccurate patient triage contributes to suboptimal clinical capacity management and delays in patient care, which in cancer patients may significantly increase morbidity and mortality. We developed a natural language processing (NLP) mode...

Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection.

Nature communications
Deep learning (DL) enabled liquid-based cytology has potential for cervical cancer screening or triage. Here, we develop a DL model using whole cytology slides from 17,397 women and test it on 10,826 additional cases through a three-stage process. Th...

Bridging Data Gaps in Emergency Care: The NIGHTINGALE Project and the Future of AI in Mass Casualty Management.

Journal of medical Internet research
In the context of mass casualty incident (MCI) management, artificial intelligence (AI) represents a promising future, offering potential improvements in processes such as triage, decision support, and resource optimization. However, the effectivenes...

Artificial intelligence versus orthopedic surgeons as an orthopedic consultant in the emergency department.

Injury
INTRODUCTION: ChatGPT, a widely accessible AI program, has demonstrated potential in various healthcare applications, including emergency department (ED) triage, differential diagnosis, and patient education. However, its potential in providing recom...