Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome - the MADDEC study.

ABSTRACT: Objective: Investigation of the clinical potential of extensive phenotype data and machine learning (ML) in the prediction of mortality in acute coronary syndrome (ACS). Methods: The value of ML and extensive clinical data was analyzed in a retrospective registry study of 9066 consecutive ACS patients (January 2007 to October 2017). Main outcome was six-month mortality. Prediction models were developed using two ML methods, logistic regression and extreme gradient boosting (xgboost). The models were fitted in training set of patients treated in 2007-2014 and 2017 (81%, n = 7344) and validated in a separate validation set of patients treated in 2015-2016 with full GRACE score data available for comparison of model accuracy (19%, n = 1722). Results: Overall

SUBMITTER: Hernesniemi JA 

PROVIDER: S-EPMC7857486 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature