Machine learning outperforms the Canadian Triage and Acuity Scale (CTAS) in predicting need for early critical care.

Journal: CJEM

PMID: 39560909

Abstract

STUDY OBJECTIVE: This study investigates the potential to improve emergency department (ED) triage using machine learning models by comparing their predictive performance with the Canadian Triage Acuity Scale (CTAS) in identifying the need for critical care within 12 h of ED arrival.

Authors

Lars Grant

Department of Emergency Medicine, McGill University, Montreal, QC, Canada.
Magueye Diagne

Department of Emergency Medicine, McGill University, Montreal, QC, Canada.
Rafael Aroutiunian

Department of Emergency Medicine, McGill University, Montreal, QC, Canada.
Devin Hopkins

Department of Emergency Medicine, McGill University, Montreal, QC, Canada.
Tian Bai

Key Laboratory of Bio-based Material Science & Technology (Northeast Forestry University), Ministry of Education, Harbin, China.
Flemming Kondrup

Quantitative Life Sciences Program, Faculty of Science, McGill University, Montreal, QC, Canada.
Gregory Clark

Department of Emergency Medicine, McGill University, Montreal, QC, Canada.

Keywords

Adult Aged Canada Critical Care Emergency Service, Hospital Female Humans Machine Learning Male Middle Aged Patient Acuity Retrospective Studies ROC Curve Triage

External Resources

View on PubMed Access via DOI PubMed (39560909)

Machine learning outperforms the Canadian Triage and Acuity Scale (CTAS) in predicting need for early critical care.

Abstract

Authors

Keywords

External Resources

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