ArticleScientific reports2021
Development and validation of a practical machine-learning triage algorithm for the detection of patients in need of critical care in the emergency department.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- Technological Integration in Triage With AI and Machine Learning Compared With Conventional Triage and Narrative Synthesis of Telehealth for Patient Prioritization in Emergency and Acute Care Settings: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- The effects of applying artificial intelligence to triage in the emergency department: A systematic review of prospective studies.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Pooled it
- Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review.BMC emergency medicine · 2024Pooled it
- Developing and validating an artificial intelligence-based electronic triage model for predicting clinical outcomes among cardiac-suspected patients in the emergency department.European heart journal. Digital health · 2026Article
- Protecting clinical value judgment in the age of AI.NPJ digital medicine · 2026Article
- Article
- Artificial intelligence-driven triage in emergency departments: a comparative study of machine learning approaches.Revista da Associacao Medica Brasileira (1992) · 2026Article
- Composite shock indices with age, GCS, and SpO2 for trauma triage outcomes: a retrospective cohort study.European journal of medical research · 2025Article
- Clinical Impact of Artificial Intelligence-Based Triage Systems in Emergency Departments: A Systematic Review.Cureus · 2025Review
- Performance measures of the medical priority dispatch system in an urban basic life support system.Scandinavian journal of trauma, resuscitation and emergency medicine · 2025Observational
- Mapping and Summarizing the Research on AI Systems for Automating Medical History Taking and Triage: Scoping Review.Journal of medical Internet research · 2025Article
- Using Machine Learning Technique in Managing Emergency Triage Flow.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2025Article
- A novel deep learning algorithm for real-time prediction of clinical deterioration in the emergency department for a multimodal clinical decision support system.Scientific reports · 2024Article
- Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review.Cureus · 2024Article
- Diagnosing an overcrowded emergency department from its Electronic Health Records.Scientific reports · 2024Article
- Interpretable Deep Learning System for Identifying Critical Patients Through the Prediction of Triage Level, Hospitalization, and Length of Stay: Prospective Study.JMIR medical informatics · 2024Article
- Inter hospital external validation of interpretable machine learning based triage score for the emergency department using common data model.Scientific reports · 2024Article
- Assessing the length of hospital stay for patients with myasthenia gravis based on the data mining MARS approach.Frontiers in neurology · 2023Article
- A Prehospital Triage System to Detect Traumatic Intracranial Hemorrhage Using Machine Learning Algorithms.JAMA network open · 2022Article
- Artificial intelligence applications in intensive care unit nursing: A narrative review (2020-2025).Digital healthReview
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying critically ill patients is a key challenge in emergency department (ED) triage. Mis-triage errors are still widespread in triage systems around the world. Here, we present a machine learning system (MLS) to assist ED triage officers better recognize critically ill patients and provide a text-based explanation of the MLS recommendation. To derive the MLS, an existing dataset of 22,272 patient encounters from 2012 to 2019 from our institution's electronic emergency triage system (EETS) was used for algorithm training and validation. The area under the receiver operating characteristic curve (AUC) was 0.875 ± 0.006 (CI:95%) in retrospective dataset using fivefold cross validation, higher than that of reference model (0.843 ± 0.005 (CI:95%)). In the prospective cohort study, compared to the traditional triage system's 1.2% mis-triage rate, the mis-triage rate in the MLS-assisted group was 0.9%. This MLS method with a real-time explanation for triage officers was able to lower the mis-triage rate of critically ill ED patients.
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