Gautam Nitesh, Ghanta Sai Nikhila, Clausen Alex, Saluja Prachi, Sivakumar Kalai, Dhar Gaurav, Chang Qi, DeMazumder Deeptankar, Rabbat Mark G, Greene Stephen J, Fudim Marat, Al'Aref Subhi J
Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
Division of Cardiology, Department of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
JACC Heart Fail. 2022 Sep;10(9):603-622. doi: 10.1016/j.jchf.2022.06.011.
Despite a better understanding of the underlying pathogenesis of heart failure (HF), pharmacotherapy, surgical, and percutaneous interventions do not prevent disease progression in all patients, and a significant proportion of patients end up requiring advanced therapies. Machine learning (ML) is gaining wider acceptance in cardiovascular medicine because of its ability to incorporate large, complex, and multidimensional data and to potentially facilitate the creation of predictive models not constrained by many of the limitations of traditional statistical approaches. With the coexistence of "big data" and novel advanced analytic techniques using ML, there is ever-increasing research into applying ML in the context of HF with the goal of improving patient outcomes. Through this review, the authors describe the basics of ML and summarize the existing published reports regarding contemporary applications of ML in device therapy for HF while highlighting the limitations to widespread implementation and its future promises.
尽管对心力衰竭（HF）的潜在发病机制有了更深入的了解，但药物治疗、手术和经皮介入治疗并不能阻止所有患者的疾病进展，仍有相当一部分患者最终需要先进的治疗方法。机器学习（ML）因其能够整合大量、复杂和多维度的数据，并有可能促进创建不受传统统计方法诸多限制约束的预测模型，而在心血管医学中得到越来越广泛的认可。随着“大数据”与使用ML的新型先进分析技术并存，为改善患者预后而将ML应用于HF的研究日益增多。通过本综述，作者描述了ML的基础知识，总结了关于ML在HF器械治疗中的当代应用的现有已发表报告，同时强调了广泛实施的局限性及其未来前景。
