ArticleHealthcare informatics research2019
Machine Learning-Based Prediction of Korean Triage and Acuity Scale Level in Emergency Department Patients.
Article in Healthcare informatics research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions.BMC medical informatics and decision making · 2025Pooled it
- Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review.BMC emergency medicine · 2024Pooled it
- Design and Feasibility Assessment of a Compact Emergency Unit in Rural and Remote Areas: A Multicenter Analysis of KTAS-Based Triage Data.Healthcare (Basel, Switzerland) · 2026Article
- Interpretable Machine Learning for Emergency Department Triage: Clinical Insights from 133,198 Patients Using the Korean Triage and Acuity Scale (KTAS).Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Performance and safety of a fine-tuned small language model for pediatric emergency triage: A benchmark study.PloS one · 2026Article
- From prediction to action: a retrospective observational study on the real-world implementation of Critical Interventions (CrIs), an AI-based clinical decision support system changing clinical behavior in the emergency department.BMC medical informatics and decision making · 2025Observational
- RETRACTED: Artificial intelligence for emergency medical care.Health care science · 2025Review
- Artificial intelligence for severity triage based on conversations in an emergency department in Korea.Scientific reports · 2025Article
- Predicting triage of pediatric patients in the emergency department using machine learning approach.International journal of emergency medicine · 2025Article
- Mapping and Summarizing the Research on AI Systems for Automating Medical History Taking and Triage: Scoping Review.Journal of medical Internet research · 2025Article
- Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases.Journal of medical systems · 2025Article
- 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
- Triage-clinical reasoning on emergency nursing competency: a multiple linear mediation effect.BMC nursing · 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
- The hatred of all against all? Evidence from online community platforms in South Korea.PloS one · 2024Article
- Clinical support system for triage based on federated learning for the Korea triage and acuity scale.Heliyon · 2023Article
- Medical emergency department triage data processing using a machine-learning solution.Heliyon · 2023Article
- A Machine Learning Approach for Monitoring and Classifying Healthcare Data-A Case of Emergency Department of KSA Hospitals.International journal of environmental research and public health · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTriage is a process to accurately assess and classify symptoms to identify and provide rapid treatment to patients. The Korean Triage and Acuity Scale (KTAS) is used as a triage instrument in all emergency centers. The aim of this study was to train and compare machine learning models to predict KTAS levels.
methodsThis was a cross-sectional study using data from a single emergency department of a tertiary university hospital. Information collected during triage was used in the analysis. Logistic regression, random forest, and XGBoost were used to predict the KTAS level.
resultsThe models with the highest area under the receiver operating characteristic curve (AUROC) were the random forest and XGBoost models trained on the entire dataset (AUROC = 0.922, 95% confidence interval 0.917-0.925 and AUROC = 0.922, 95% confidence interval 0.918-0.925, respectively). The AUROC of the models trained on the clinical data was higher than that of models trained on text data only, but the models trained on all variables had the highest AUROC among similar machine learning models.
conclusionsMachine learning can robustly predict the KTAS level at triage, which may have many possibilities for use, and the addition of text data improves the predictive performance compared to that achieved by using structured data alone.
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