Scripps Research Translational Institute, La Jolla, California, USA. Electronic address: https://twitter.com/giorgioquer.
Division of Clinical Pathology, Department of Pathology, Beth Israel Deaconess Medical Center, Beth Israel Lahey Health, Boston, Massachusetts, USA.
J Am Coll Cardiol. 2021 Jan 26;77(3):300-313. doi: 10.1016/j.jacc.2020.11.030.
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.
医生的角色一直是综合他们所掌握的资料，以识别诊断模式，从而指导治疗并跟踪反应。如今，日益复杂的机器学习算法可能会在这些任务中帮助临床专家。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应用于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中的关键概念，使他们能够更好地理解该领域，并评估新的文献和进展。然后，使用文献计量调查以及公开代码（可用于对任何感兴趣的研究课题进行类似分析），总结了目前机器学习在心血管疾病中的已有数据，并选择了一些案例研究。最后，提出了临床医生可以并且必须参与这一新兴领域的几种方式。
