High Quality Phenotypic Data and Machine Learning Beat a Generic Risk Score in the Prediction of Mortality in Acute Coronary Syndrome.

We set out to find out if models developed with a hospital's own data beat a current state-of-the art risk predictor for mortality in acute coronary syndrome. Our data of 9,066 patients was collected and integrated from operational clinical electronic health records. Our best classifier, XGBoost, achieved a performance of AUC 0.890 and beat the current generic gold standard, GRACE (AUC 0.822).