Prediction of infection in the emergency department—a machine learning model
Thomas Kronborg
BackgroundThe integration of artificial intelligence (AI) into emergency medicine holds promise for enhancing early diagnosis and clinical decision-making. Traditional diagnostic approaches often rely on physician judgment or early warning scores that may delay identification of infections, especially when these tools prioritize outcomes such as mortality or ICU admission over early infection detection.ObjectiveThis study aimed to identify the most informative predictors of infection at emergenc