机器学习方法识别缺血性心力衰竭伴射血分数降低患者的表型

IF 16.9 1区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS

European Journal of Heart Failure Pub Date : 2024-12-10 DOI:10.1002/ejhf.3547

Luca Monzo, Emmanuel Bresso, Kenneth Dickstein, Bertram Pitt, John G.F. Cleland, Stefan D. Anker, Carolyn S.P. Lam, Mandeep R. Mehra, Dirk J. van Veldhuisen, Barry Greenberg, Faiez Zannad, Nicolas Girerd

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引用次数: 0

摘要

缺血性心力衰竭伴射血分数降低（HFrEF）的患者是一个不同的群体。我们假设机器学习聚类可以帮助分离不同的患者表型，为个性化管理铺平道路。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

$Machine learning approach to identify phenotypes in patients with ischaemic heart failure with reduced ejection fraction$

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Machine learning approach to identify phenotypes in patients with ischaemic heart failure with reduced ejection fraction

Patients experiencing ischaemic heart failure with reduced ejection fraction (HFrEF) represent a diverse group. We hypothesize that machine learning clustering can help separate distinctive patient phenotypes, paving the way for personalized management.

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来源期刊

European Journal of Heart Failure 医学-心血管系统

CiteScore

27.30

自引率

11.50%

发文量

365

审稿时长

1 months

期刊介绍： European Journal of Heart Failure is an international journal dedicated to advancing knowledge in the field of heart failure management. The journal publishes reviews and editorials aimed at improving understanding, prevention, investigation, and treatment of heart failure. It covers various disciplines such as molecular and cellular biology, pathology, physiology, electrophysiology, pharmacology, clinical sciences, social sciences, and population sciences. The journal welcomes submissions of manuscripts on basic, clinical, and population sciences, as well as original contributions on nursing, care of the elderly, primary care, health economics, and other related specialist fields. It is published monthly and has a readership that includes cardiologists, emergency room physicians, intensivists, internists, general physicians, cardiac nurses, diabetologists, epidemiologists, basic scientists focusing on cardiovascular research, and those working in rehabilitation. The journal is abstracted and indexed in various databases such as Academic Search, Embase, MEDLINE/PubMed, and Science Citation Index.