ArticleScientific reports2024
Leveraging graph neural networks for supporting automatic triage of patients.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Shaping the Future of Men's Health: How AI Could Be a Transformative Tool for Better Patient Outcomes and Provider Efficiency.Journal of medical Internet research · 2026Article
- A hybrid AHP and K-means model for biopsychosocial surgical prioritization: validation in a high-complexity ENT unit.Clinics (Sao Paulo, Brazil) · 2026Article
- From data to decisions: a modular platform for modelling and simulation of infectious disease diffusion in networks.BMC medical informatics and decision making · 2026Article
- Artificial intelligence in emergency department triage: a scoping review on workload reduction and patient safety enhancement.Journal of Korean biological nursing science · 2025Review
- Research on the online service mechanism of internet hospital in infectious disease prevention and control.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Feasibility of Mental Health Triage Call Priority Prediction Using Machine Learning.Nursing reports (Pavia, Italy) · 2024Article
- Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education.Turkish journal of emergency medicineReview
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patient triage is crucial in emergency departments, ensuring timely and appropriate care based on correctly evaluating the emergency grade of patient conditions. Triage methods are generally performed by human operator based on her own experience and information that are gathered from the patient management process. Thus, it is a process that can generate errors in emergency-level associations. Recently, Traditional triage methods heavily rely on human decisions, which can be subjective and prone to errors. A growing interest has recently been focused on leveraging artificial intelligence (AI) to develop algorithms to maximize information gathering and minimize errors in patient triage processing. We define and implement an AI-based module to manage patients' emergency code assignments in emergency departments. It uses historical data from the emergency department to train the medical decision-making process. Data containing relevant patient information, such as vital signs, symptoms, and medical history, accurately classify patients into triage categories. Experimental results demonstrate that the proposed algorithm achieved high accuracy outperforming traditional triage methods. By using the proposed method, we claim that healthcare professionals can predict severity index to guide patient management processing and resource allocation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.