Unsupervised machine learning analysis to enhance risk stratification in patients with asymptomatic aortic stenosis

Artificial intelligence-guided phenotypic classification revealed 5 distinct groups and enhanced risk stratification of patients with AS. This approach may be useful to optimize and individualize medical and interventional management of AS.

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Publication Details

Journal
European Heart Journal - Digital Health
Published
2025-10-07
DOI
https://doi.org/10.1093/ehjdh/ztaf115
Primary Topic
Cardiac Valve Diseases and Treatments
Type
article
Field-Weighted Citation Impact
0.00
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article

Unsupervised machine learning analysis to enhance risk stratification in patients with asymptomatic aortic stenosis

Marie Arsenault, Mickaël Leclercq, Pierre‐Alexandre Mattei, Fŕed́eric Precioso et al.
European Heart Journal - Digital Health
Cardiac Valve Diseases and Treatments
article

Unsupervised machine learning analysis to enhance risk stratification in patients with asymptomatic aortic stenosis

Marie Arsenault, Mickaël Leclercq, Pierre‐Alexandre Mattei, Fŕed́eric Precioso, Sébastien Hecht, Philippe Pîbarot, Lionel Tastet, Romain Capoulade, Louis Ohl, Erwan Salaün, Jonathan Beaudoin, Jérémy Bernard, Arnaud Droit, Marie‐Ange Fleury, Mathieu Bernier, Nancy Côté, Kathia Abdoun, Élisabeth Bédard, Mylène Shen
article en

Abstract

Artificial intelligence-guided phenotypic classification revealed 5 distinct groups and enhanced risk stratification of patients with AS. This approach may be useful to optimize and individualize medical and interventional management of AS.

European Heart Journal - Digital HealthVol. 7(1)
Linköping University (SE), Centre National de la Recherche Scientifique (FR), Inserm (FR), University of California, San Francisco (US), Centre Hospitalier Universitaire de Nantes (FR), University of California San Diego (US), Institut universitaire de cardiologie et de pneumologie de Québec (CA), Centre hospitalier de l'Université Laval (CA), Institut du Thorax (FR), Institut de Physique de Nice (FR), Université Laval (CA), University of San Francisco (US), Nantes Université (FR)
Openalex Percentile: Top 57%
Cardiac Valve Diseases and Treatments
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