Observational studyPloS one2018
Validation of deep-learning-based triage and acuity score using a large national dataset.
Observational study in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 2 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
46 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- 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
- Understanding uncertainty in large language model predictions of early death in critically ill patients: a conformal prediction approach.JAMIA open · 2026Article
- Machine learning models for early mortality prediction in trauma patients using public data: a nationwide retrospective study.World journal of emergency surgery : WJES · 2026Article
- Article
- Understanding Uncertainty in Large Language Model Predictions of Early Death in Critically Ill Patients: A Conformal Prediction Approach.medRxiv : the preprint server for health sciences · 2025Article
- Machine Learning-Driven Integration of Cancer Cell Phenotypes Predicts Cisplatin Sensitivity.Cancer medicine · 2025Article
- 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
- Mapping and Summarizing the Research on AI Systems for Automating Medical History Taking and Triage: Scoping Review.Journal of medical Internet research · 2025Article
- External validation of an artificial intelligence model using clinical variables, including ICD-10 codes, for predicting in-hospital mortality among trauma patients: a multicenter retrospective cohort study.Scientific reports · 2025Article
- Using machine learning and natural language processing in triage for prediction of clinical disposition in the emergency department.BMC emergency medicine · 2024Article
- Identification of avoidable patients at triage in a Paediatric Emergency Department: a decision support system using predictive analytics.BMC emergency medicine · 2024Article
- Who should decide how limited healthcare resources are prioritized? Autonomous technology as a compelling alternative to humans.PloS one · 2024Article
- Clinical support system for triage based on federated learning for the Korea triage and acuity scale.Heliyon · 2023Article
- Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department.Scientific reports · 2023Article
- Continuous Data-Driven Monitoring in Critical Congenital Heart Disease: Clinical Deterioration Model Development.JMIR cardio · 2023Article
- Machine Learning-Based Models for Prediction of Critical Illness at Community, Paramedic, and Hospital Stages.Emergency medicine international · 2023Article
- Applications of natural language processing at emergency department triage: A narrative review.PloS one · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTriage is important in identifying high-risk patients amongst many less urgent patients as emergency department (ED) overcrowding has become a national crisis recently. This study aims to validate that a Deep-learning-based Triage and Acuity Score (DTAS) identifies high-risk patients more accurately than existing triage and acuity scores using a large national dataset.
methodsWe conducted a retrospective observational cohort study using data from the Korean National Emergency Department Information System (NEDIS), which collected data on visits in real time from 151 EDs. The NEDIS data was split into derivation data (January 2014-June 2016) and validation data (July-December 2016). We also used data from the Sejong General Hospital (SGH) for external validation (January-December 2017). We predicted in-hospital mortality, critical care, and hospitalization using initial information of ED patients (age, sex, chief complaint, time from symptom onset to ED visit, arrival mode, trauma, initial vital signs and mental status as predictor variables).
resultsA total of 11,656,559 patients were included in this study. The primary outcome was in-hospital mortality. The Area Under the Receiver Operating Characteristic curve (AUROC) and Area Under the Precision and Recall Curve (AUPRC) of DTAS were 0.935 and 0.264. It significantly outperformed Korean triage and acuity score (AUROC:0.785, AUPRC:0.192), modified early warning score (AUROC:0.810, AUPRC:0.116), logistic regression (AUROC:0.903, AUPRC:0.209), and random forest (AUROC:0.910, AUPRC:0.179).
conclusionDeep-learning-based Triage and Acuity Score predicted in-hospital mortality, critical care, and hospitalization more accurately than existing triages and acuity, and it was validated using a large, multicenter dataset.
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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.