ArticleScientific reports2025
Development and internal validation of an AI-based emergency triage model for predicting critical outcomes in emergency department.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
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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
10 citing papers in PubMed.
- Artificial Intelligence Readiness in Emergency Medicine: Expert Consensus Opinion for Preparing the Workforce.Journal of the American College of Emergency Physicians open · 2026Article
- Article
- Adding Artificial Intelligence to the Surgeon's Domain.World journal of surgery · 2026Review
- Beyond diagnosis: deep-learning-based analysis of hospitalization using abdominal radiographs in the emergency department.Abdominal radiology (New York) · 2026Article
- Advancing emergency medicine across Asia: a roadmap for leadership, collaboration, and digital transformation.International journal of emergency medicine · 2026Article
- Optimising Retraining Frequency for a Paediatric Emergency Department Admission Prediction Model: Development and Temporal Validation Using Real-World Data.Emergency medicine Australasia : EMA · 2026Article
- Optimized explainable AI and digital twin for patient flow improvement in ICU during respiratory epidemics.BMC medical informatics and decision making · 2026Article
- [Primary assessment of urological emergencies using the example of a specialist hospital].Urologie (Heidelberg, Germany) · 2026Article
- The Role of Artificial Intelligence in Enhancing Diagnostic and Triage Accuracy in Hospital Emergency Departments: A Systematic Review.Medical journal of the Islamic Republic of Iran · 2026Review
- 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
4 authors.
Funding
Abstract
Emergency department (ED) overcrowding contributes to delayed patient care and worse clinical outcomes. Traditional triage systems face accuracy and consistency limitations. This study developed and internally validated a machine learning model predicting intensive care unit (ICU) admissions and resource utilization in ED patients. A retrospective analysis of 163,452 ED visits (2018-2022) from Maharaj Nakhon Chiang Mai Hospital evaluated logistic regression, random forest, and XGBoost models against the Canadian Triage and Acuity Scale (CTAS). The XGBoost model achieved superior predictive performance (AUROC 0.917 vs. 0.882, AUPRC 0.629 vs. 0.333). Key predictors included mode of arrival, patient age, and free-text chief complaints analyzed with multilingual sentence embeddings. These results demonstrate that machine learning, incorporating unstructured text data, has the potential to enhance triage accuracy and resource allocation by more effectively identifying critically ill patients compared to traditional triage methods.
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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.