ArticleHealthcare informatics research2020
Machine Learning and Initial Nursing Assessment-Based Triage System for Emergency Department.
Article in Healthcare informatics research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review.BMC emergency medicine · 2024Pooled it
- Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review.BMC health services research · 2022Pooled it
- RETRACTED: Artificial intelligence for emergency medical care.Health care science · 2025Review
- Artificial intelligence in emergency department triage: a scoping review on workload reduction and patient safety enhancement.Journal of Korean biological nursing science · 2025Review
- Comparison of Multimodal Deep Learning Approaches for Predicting Clinical Deterioration in Ward Patients: Observational Cohort Study.Journal of medical Internet research · 2025Observational
- Designing Health Care Provider-Centered Emergency Department Interventions: Participatory Design Study.JMIR formative research · 2025Article
- Mapping and Summarizing the Research on AI Systems for Automating Medical History Taking and Triage: Scoping Review.Journal of medical Internet research · 2025Article
- Exploring Applications of Artificial Intelligence in Critical Care Nursing: A Systematic Review.Nursing reports (Pavia, Italy) · 2025Review
- Using machine learning and natural language processing in triage for prediction of clinical disposition in the emergency department.BMC emergency medicine · 2024Article
- Leveraging graph neural networks for supporting automatic triage of patients.Scientific reports · 2024Article
- Inter hospital external validation of interpretable machine learning based triage score for the emergency department using common data model.Scientific reports · 2024Article
- Admission prioritization of heart failure patients with multiple comorbidities.Frontiers in digital health · 2024Article
- Artificial Intelligence in Optimizing the Functioning of Emergency Departments; a Systematic Review of Current Solutions.Archives of academic emergency medicine · 2024Review
- ChatGPT's performance before and after teaching in mass casualty incident triage.Scientific reports · 2023Article
- The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review.Archives of academic emergency medicine · 2023Article
- An external validation study of the Score for Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency department.Scientific reports · 2022Article
- Effect of Applying a Real-Time Medical Record Input Assistance System With Voice Artificial Intelligence on Triage Task Performance in the Emergency Department: Prospective Interventional Study.JMIR medical informatics · 2022Article
- Major Adverse Events in Patients with Peripheral Artery Disease after Endovascular Revascularization: A Retrospective Study.Journal of clinical medicine · 2022Article
- Development and Validation of Simple Age-Adjusted Objectified Korean Triage and Acuity Scale for Adult Patients Visiting the Emergency Department.Yonsei medical journal · 2022Article
- Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review.Journal of medical Internet research · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe aim of this study was to develop machine learning (ML) and initial nursing assessment (INA)-based emergency department (ED) triage to predict adverse clinical outcome.
methodsThe retrospective study included ED visits between January 2016 and December 2017 that resulted in either intensive care unit admission or emergency room death. We trained four classifiers using logistic regression and a deep learning model on INA and low dimensional (LD) INA, logistic regression on the Korea Triage and acuity scale (KTAS) and Sequential Related Organ Failure Assessment (SOFA). We varied the outcome ratio for external validation. Finally, variables of importance were identified using the random forest model's information gain. The four most influential variables were used for LD modeling for efficiency.
resultsA total of 86,304 patient visits were included, with an overall outcome rate of 3.5%. The area under the curve (AUC) values for the KTAS model were 76.8 (74.9-78.6) with logistic regression and 74.0 (72.1-75.9) for the SOFA model, while the AUC values of the INA model were 87.2 (85.9-88.6) and 87.6 (86.3-88.9) with logistic regression and deep learning, suggesting that the ML and INA-based triage system result more accurately predicted the outcomes. The AUC values for the LD model were 81.2 (79.4-82.9) and 80.7 (78.9-82.5) for logistic regression and deep learning, respectively.
conclusionsWe developed an ML and INA-based triage system for EDs. The novel system was able to predict clinical outcomes more accurately than existing triage systems, KTAS and SOFA.
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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.