Machine-learned models using hematological inflammation markers in the prediction of short-term acute coronary syndrome outcomes

Machine learned models can rely on the association between the elevated inflammatory markers and the short-term ACS outcomes to provide accurate predictions. Moreover, such models can help assess the usefulness of laboratory and clinical features in predicting the in-hospital mortality of ACS patients.