机器学习在心脏电生理中的应用。

IF 2.6 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Arrhythmia & Electrophysiology Review Pub Date : 2020-08-01 DOI:10.15420/aer.2019.19
Rahul G Muthalaly, Robert M Evans
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引用次数: 7

摘要

通过机器学习(ML)方法实现的人工智能在全球变得越来越普遍,在医疗保健领域的应用也越来越多。技术的进步使机器学习的早期应用能够帮助医生提高效率和诊断准确性。在电生理学中,ML在病人护理的每个阶段都有应用。然而,它的使用仍处于起步阶段。本文将介绍机器学习的潜力,然后讨论大数据的概念及其缺陷。作者回顾了一些常见的机器学习方法,包括监督学习和无监督学习,然后研究了在心脏电生理中的应用。这将集中在表面心电图,心内测绘和心脏植入式电子设备。最后,文章总结了机器学习在未来对电生理的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Applications of Machine Learning in Cardiac Electrophysiology.

Artificial intelligence through machine learning (ML) methods is becoming prevalent throughout the world, with increasing adoption in healthcare. Improvements in technology have allowed early applications of machine learning to assist physician efficiency and diagnostic accuracy. In electrophysiology, ML has applications for use in every stage of patient care. However, its use is still in infancy. This article will introduce the potential of ML, before discussing the concept of big data and its pitfalls. The authors review some common ML methods including supervised and unsupervised learning, then examine applications in cardiac electrophysiology. This will focus on surface electrocardiography, intracardiac mapping and cardiac implantable electronic devices. Finally, the article concludes with an overview of how ML may impact on electrophysiology in the future.

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来源期刊
Arrhythmia & Electrophysiology Review
Arrhythmia & Electrophysiology Review CARDIAC & CARDIOVASCULAR SYSTEMS-
CiteScore
5.10
自引率
6.70%
发文量
22
审稿时长
7 weeks
期刊最新文献
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