Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome.

ABSTRACT: Most risk stratification methods use expert opinion to identify a fixed number of clinical variables that have prognostic significance. In this study our goal was to develop improved metrics that utilize a variable number of input parameters. We first used Bootstrap Lasso Regression (BLR) - a Machine Learning method for selecting important variables - to identify a prognostic set of features that identify patients at high risk of death 6-months after presenting with an Acute Coronary Syndrome. Using data derived from the Global Registry of Acute Coronary Events (GRACE) we trained a logistic regression model using these features and evaluated its performance on a development set (N = 43,063) containing patients who have values for all features, and a separate dataset (N = 6,363) that contai

SUBMITTER: Myers PD 

PROVIDER: S-EPMC6787006 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature