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.

中文翻译：

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机器学习和心血管护理的未来

医生的作用一直是综合他们可用的数据，以确定指导治疗和跟踪反应的诊断模式。今天，越来越复杂的机器学习算法可能会发展为支持临床专家完成其中一些任务。机器学习有可能使患者和心脏病专家受益，但前提是临床医生在将这些新算法付诸实践中发挥积极作用。本综述的目的是向非数据科学专家的临床医生介绍机器学习的关键概念，使他们能够更好地了解该领域并评估新的文献和发展。然后使用文献计量调查总结当前发表的心血管疾病机器学习数据，使用公开可用的代码，以便对任何感兴趣的研究主题进行类似的分析，并选择案例研究。最后，介绍了临床医生可以而且必须参与这一新兴领域的几种方式。