首页|McGill University Reports Findings in Machine Learning [Machi ne learning outperforms the Canadian Triage and Acuity Scale (CTAS) in predictin g need for early critical care]
McGill University Reports Findings in Machine Learning [Machi ne learning outperforms the Canadian Triage and Acuity Scale (CTAS) in predictin g need for early critical care]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to newsreporting originating in Montreal, Cana da, by NewsRx journalists, research stated, “This study investigatesthe potenti al to improve emergency department (ED) triage using machine learning models by comparingtheir predictive performance with the Canadian Triage Acuity Scale (CT AS) in identifying the need forcritical care within 12 h of ED arrival. Three m achine learning models (LASSO regression, gradientboostedtrees, and a deep lea rning model with embeddings) were developed using retrospective data from670,84 1 ED visits to the Jewish General Hospital from June 2012 to Jan 2021.”
MontrealCanadaNorth and Central Amer icaCritical Care MedicineCyborgsEmerging TechnologiesHealth and MedicineMachine Learning
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