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

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

引用次数: 0

Abstract

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.