Machine learning approaches improve risk stratification for secondary cardiovascular disease prevention in multiethnic patients.

Journal: Open heart
Published Date: Oct 1, 2021

Abstract

OBJECTIVES: Identifying high-risk patients is crucial for effective cardiovascular disease (CVD) prevention. It is not known whether electronic health record (EHR)-based machine-learning (ML) models can improve CVD risk stratification compared with a secondary prevention risk score developed from randomised clinical trials (Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention, TRS 2°P).

Authors

  • Ashish Sarraju
    Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, Ohio, USA.
  • Andrew Ward
    From the Department of Electrical Engineering, Stanford University, Stanford, California.
  • Sukyung Chung
    Palo Alto Medical Foundation Research Institute, Palo Alto, California, USA.
  • Jiang Li
  • David Scheinker
    Department of Management Science and Engineering (D.S.), Stanford University, CA.
  • Fatima Rodriguez
    From the Division of Cardiovascular Medicine, Cardiovascular Institute (F.R., R.A.H.), Department of Medicine (F.R., R.A.H.).

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