Determinants of In-Hospital Mortality After Percutaneous Coronary Intervention: A Machine Learning Approach.

ABSTRACT: Background The ability to accurately predict the occurrence of in-hospital death after percutaneous coronary intervention is important for clinical decision-making. We sought to utilize the New York Percutaneous Coronary Intervention Reporting System in order to elucidate the determinants of in-hospital mortality in patients undergoing percutaneous coronary intervention across New York State. Methods and Results We examined 479 804 patients undergoing percutaneous coronary intervention between 2004 and 2012, utilizing traditional and advanced machine learning algorithms to determine the most significant predictors of in-hospital mortality. The entire data were randomly split into a training (80%) and a testing set (20%). Tuned hyperparameters were used to generate a trained model while the

SUBMITTER: Al'Aref SJ 

PROVIDER: S-EPMC6474922 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature