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Common causes of death in hospitals, such as sepsis and respiratory failure, are treatable and benefit from early intervention. Machine learning algorithms or early warning scores can be used for early identification and recognition to potentially help accelerate interventions and limit morbidity and mortality. This Concise Critical Appraisal explores an article published in Critical Care Medicine that looked at the impact of one of these early warning scores—electronic cardiac arrest risk triage (eCART)—on mortality for elevated-risk adult inpatients.
This podcast discusses a novel machine learning model that identifies ICU transfers in hospitalized children more accurately than current tools. The discussion centers on the article “Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU,” published in the July 2022 issue of Pediatric Critical Care Medicine.