aceArtificial intelligence-driven triage in emergency departments: a comparative study of machine learning approaches
Objective In emergency departments, accurate and timely triage plays a critical role in prioritizing patient care. Traditional triage often suffers from subjectivity and inconsistency, especially under time pressure and overcrowded conditions. The aim of this study was to evaluate the predictive potential of multiple supervised machine learning models for three-class emergency department triage classification and compare their performance using routinely available triage variables. Methods We conducted a compara…
Several supervised models, including logistic regression, random forest, support vector machine and majority voting, classified three-level triage urgency with accuracy above 89% using routine triage variables.
The analysis used a small retrospective dataset of 510 records, and models struggled to separate high-urgency from medium-urgency cases, so real-world workflow benefits are not demonstrated.
Evidence
- Peer-reviewedRevista da Associação Médica Brasileira2026-10-05
How should this claim be treated?
Truvace Impact Record TRV-2026-1841, v1: “Artificial intelligence-driven triage in emergency departments: a comparative study of machine learning approaches.” Truvace, 2026-10-18. /record/TRV-2026-1841 (accessed at citation time). sha256 0bf377e0ab5405d5…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1841 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.