Machine learning compared with rule-in/rule-out algorithms and logistic regression to predict acute myocardial infarction based on troponin T concentrations
ObjectiveComputerized decision‐support tools may improve diagnosis of acute myocardial infarction (AMI) among patients presenting with chest pain at the emergency department (ED). The primary aim was to assess the predictive accuracy of machine learning algorithms based on paired high‐sensitivity cardiac troponin T (hs‐cTnT) concentrations with varying sampling times, age, and sex in order to rule
