Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction.

ABSTRACT: OBJECTIVE:Conventional risk stratification models for mortality of acute myocardial infarction (AMI) have potential limitations. This study aimed to develop and validate deep-learning-based risk stratification for the mortality of patients with AMI (DAMI). METHODS:The data of 22,875 AMI patients from the Korean working group of the myocardial infarction (KorMI) registry were exclusively divided into 12,152 derivation data of 36 hospitals and 10,723 validation data of 23 hospitals. The predictor variables were the initial demographic and laboratory data. The endpoints were in-hospital mortality and 12-months mortality. We compared the DAMI performance with the global registry of acute coronary event (GRACE) score, acute coronary treatment and intervention outcomes network (ACTION) score, an

SUBMITTER: Kwon JM 

PROVIDER: S-EPMC6822714 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature