Machine Learning and the Future of Cardiovascular Care JACC State-of-the-Art Review

被引:256
作者
Quer, Giorgio [1 ]
Arnaout, Ramy [2 ]
Henne, Michael [3 ]
Arnaout, Rima [4 ]
机构
[1] Scripps Res Translat Inst, La Jolla, CA USA
[2] Beth Israel Deaconess Med Ctr, Dept Pathol, Div Clin Pathol, Beth Israel Lahey Hlth, 330 Brookline Ave, Boston, MA 02215 USA
[3] Univ Calif San Francisco, Dept Med, Div Cardiol, San Francisco, CA 94143 USA
[4] Univ Calif San Francisco, Dept Med, Div Cardiol, Bakar Computat Hlth Sci Inst,Ctr Intelligent Imag, 555 Mission Bay Blvd South, San Francisco, CA 94158 USA
基金
美国国家卫生研究院; 美国国家科学基金会;
关键词
artificial intelligence; bibliometric analysis; cardiology; deep learning; literature search; machine learning; ARTIFICIAL-INTELLIGENCE; HEART-FAILURE; CLASSIFICATION; VALIDATION; CARDIOLOGY;
D O I
10.1016/j.jacc.2020.11.030
中图分类号
R5 [内科学];
学科分类号
100201 [内科学];
摘要
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. (C) 2021 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation.
引用
收藏
页码:300 / 313
页数:14
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