Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts.

ABSTRACT:

Background

Binary diagnosis of coronary artery disease does not preserve the complexity of disease or quantify its severity or its associated risk with death; hence, a quantitative marker of coronary artery disease is warranted. We evaluated a quantitative marker of coronary artery disease derived from probabilities of a machine learning model.

Methods

In this cohort study, we developed and validated a coronary artery disease-predictive machine learning model using 95 935 electronic health records and assessed its probabilities as in-silico scores for coronary artery disease (ISCAD; range 0 [lowest probability] to 1 [highest probability]) in participants in two longitudinal biobank cohorts. We measured the association of ISCAD with clinical outcomes-namely, coronary artery s

SUBMITTER: Forrest IS 

PROVIDER: S-EPMC10069625 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature