Artificial Intelligence in Precision Cardiovascular Medicine

被引:705
作者
Krittanawong, Chayakrit [1 ,2 ]
Zhang, HongJu [3 ]
Wang, Zhen [4 ,5 ]
Aydar, Mehmet [2 ,6 ]
Kitai, Takeshi [2 ,7 ]
机构
[1] Icahn Sch Med Mt Sinai, Dept Internal Med, 1000 10th Ave, New York, NY 10019 USA
[2] Cleveland Clin, Inst Heart & Vasc, Dept Cardiovasc Med, Cleveland, OH 44106 USA
[3] Mayo Clin, Dept Med, Div Cardiovasc Dis, Rochester, MN USA
[4] Robert D & Patricia E Kern Ctr Sci Hlth Care Deli, Rochester, MN USA
[5] Mayo Clin, Dept Hlth Sci Res, Div Hlth Care Policy & Res, Rochester, MN USA
[6] Kent State Univ, Dept Comp Sci, Kent, OH 44242 USA
[7] Gen Hosp, Kobe City Med Ctr, Dept Cardiovasc Med, Kobe, Hyogo, Japan
关键词
big data; cognitive computing; deep learning; machine learning; CORONARY-ARTERY-DISEASE; HEART-FAILURE; SUPPORT; CLASSIFICATION; PREDICTION; VALIDATION; SYSTEM;
D O I
10.1016/j.jacc.2017.03.571
中图分类号
R5 [内科学];
学科分类号
100201 [内科学];
摘要
Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates. Over the past decade, several machine-learning techniques have been used for cardiovascular disease diagnosis and prediction. Each problem requires some degree of understanding of the problem, in terms of cardiovascular medicine and statistics, to apply the optimal machine-learning algorithm. In the near future, AI will result in a paradigm shift toward precision cardiovascular medicine. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI's application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine. (J Am Coll Cardiol 2017;69:2657-64) (C) 2017 by the American College of Cardiology Foundation.
引用
收藏
页码:2657 / 2664
页数:8
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