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Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review.
Quer, Giorgio; Arnaout, Ramy; Henne, Michael; Arnaout, Rima.
Afiliação
  • Quer G; Scripps Research Translational Institute, La Jolla, California, USA. Electronic address: https://twitter.com/giorgioquer.
  • Arnaout R; Division of Clinical Pathology, Department of Pathology, Beth Israel Deaconess Medical Center, Beth Israel Lahey Health, Boston, Massachusetts, USA.
  • Henne M; Department of Medicine, Division of Cardiology, University of California, San Francisco, California, USA.
  • Arnaout R; Department of Medicine, Division of Cardiology, Bakar Computational Health Sciences Institute, Center for Intelligent Imaging, University of California, San Francisco, California, USA. Electronic address: rima.arnaout@ucsf.edu.
J Am Coll Cardiol ; 77(3): 300-313, 2021 01 26.
Article em En | MEDLINE | ID: mdl-33478654
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cardiologia / Aprendizado de Máquina Tipo de estudo: Qualitative_research Limite: Humans Idioma: En Revista: J Am Coll Cardiol Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cardiologia / Aprendizado de Máquina Tipo de estudo: Qualitative_research Limite: Humans Idioma: En Revista: J Am Coll Cardiol Ano de publicação: 2021 Tipo de documento: Article