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      Machine Learning and the Future of Cardiovascular Care

      , , ,
      Journal of the American College of Cardiology
      Elsevier BV

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          Abstract

          The role of physicians has always been to synthesize the data available to them to identify diagnostic patterns that guide treatment and follow response. Today, increasingly sophisticated machine learning algorithms may grow to support clinical experts in some of these tasks. Machine learning has the potential to benefit patients and cardiologists, but only if clinicians take an active role in bringing these new algorithms into practice. The aim of this review is to introduce clinicians who are not data science experts to key concepts in machine learning that will allow them to better understand the field and evaluate new literature and developments. The current published data in machine learning for cardiovascular disease is then summarized, using both a bibliometric survey, with code publicly available to enable similar analysis for any research topic of interest, and select case studies. Finally, several ways that clinicians can and must be involved in this emerging field are presented.

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          Author and article information

          Journal
          Journal of the American College of Cardiology
          Journal of the American College of Cardiology
          Elsevier BV
          07351097
          January 2021
          January 2021
          : 77
          : 3
          : 300-313
          Article
          10.1016/j.jacc.2020.11.030
          7839163
          33478654
          c4fc3314-75e1-49ba-8305-52f6062860bf
          © 2021

          https://www.elsevier.com/tdm/userlicense/1.0/

          http://creativecommons.org/licenses/by-nc-nd/4.0/

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