Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-1841
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-18T18:13:31.640784Z
status: published
lens: g_space
sector: health
headline: Artificial intelligence-driven triage in emergency departments: a comparative study of machine learning approaches
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: logistic regression, random forest, support vector machine, and majority voting achieved the highest predictive performance
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers compared eight supervised machine learning models for sorting emergency department patients into three urgency levels. They used 510 retrospective triage records containing demographics, vital signs and chief complaints, and tested each model with 10-fold cross-validation.

The findings suggest routine triage data may be enough for algorithms to support urgency decisions, which matters where triage is subjective and departments are crowded. However, the evidence comes from retrospective records rather than live use, so effects on patient flow or care quality remain untested.
limitation: 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.
tag: Evidence-backed gain
key_points: Eight supervised machine learning models were compared on a three-class triage task. | The dataset held 510 retrospective emergency department triage records. | Inputs were demographic features, vital signs and chief complaints. | Logistic regression, random forest, support vector machine and majority voting performed best, above 89% accuracy. | Low-urgency cases were classified most accurately, while errors mostly occurred between high and medium urgency.
rundown: The study was motivated by concerns that traditional triage can be subjective and inconsistent, particularly under time pressure and overcrowding. It framed accurate and timely triage as central to prioritizing patient care.

Performance was judged with accuracy, precision, recall and F1-score under 10-fold cross-validation. Confusion matrix analysis showed low-urgency cases were handled best across all models.

The main weakness was distinguishing high-urgency from medium-urgency patients, where most misclassifications occurred. The authors conclude that such models appear well-suited to support triage decisions and could improve prioritization and clinical operations if integrated into workflows.
sources:
- peer_reviewed | Revista da Associação Médica Brasileira | https://doi.org/10.1590/1806-9282.20260287 | 2026-10-05
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

0bf377e0ab5405d5f299b3bd809b43e3e64414bbd4ae3dd89ba73688799d24ce

previous

0000000000000000000000000000000000000000000000000000000000000000