Development and External Validation of a Deep Learning Algorithm for Prognostication of Cardiovascular Outcomes.

ABSTRACT:

Background and objectives

We aim to explore the additional discriminative accuracy of a deep learning (DL) algorithm using repeated-measures data for identifying people at high risk for cardiovascular disease (CVD), compared to Cox hazard regression.

Methods

Two CVD prediction models were developed from National Health Insurance Service-Health Screening Cohort (NHIS-HEALS): a Cox regression model and a DL model. Performance of each model was assessed in the internal and 2 external validation cohorts in Koreans (National Health Insurance Service-National Sample Cohort; NHIS-NSC) and in Europeans (Rotterdam Study). A total of 412,030 adults in the NHIS-HEALS; 178,875 adults in the NHIS-NSC; and the 4,296 adults in Rotterdam Study were included.

Results

Mean ages was 52

SUBMITTER: Cho IJ 

PROVIDER: S-EPMC6923233 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature