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Identification of atrial fibrillation differential clinical risk phenotypes in the GLORIA-AF Registry: An analysis using Generative Topographic Mapping, a novel machine learning approach to clustering

28 August 2026 (15:15 - 16:00)
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Congress Presentation Part of: Artificial intelligence for cardiovascular phenotyping and precision risk prediction Machine Learning ESC2026 Premium Access ESC Congress 2026 European Society of Cardiology

About the speaker

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Miss Wenhui Li

University of Exeter, Exeter (United Kingdom of Great Britain & Northern Ireland)

7 More presentations in this session

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Leveraging machine learning approaches to identify behavioural and lifestyle risk factors and predict mortality among people with cardiovascular disease
Ms R. Cong (AU)
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Development and external validation of a novel echocardiography-based AI model for predicting aortic stenosis progression
Doctor K. Faierstein (Ramat Gan, IL)
Abstract Slides
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Automated view selection tool delivers high accuracy across standard and handheld echocardiography: results from a government-funded project
Miss D. Pelin (Wurzburg, DE)
Abstract Slides
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Interpretable dynamic graph learning for low-frequency cohorts: mapping pseudotime windows to CKM network dynamics
Doctor T. Liu (Tianjin, CN)
Abstract Slides
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Proteomics-informed reconstruction of myocardial metabolism reveals profound bioenergetic failure in patients with anthracycline-induced cardiotoxicity
Doctor N. Beyhoff (Berlin, DE)
Abstract Slides

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Artificial intelligence for cardiovascular phenotyping and precision risk prediction

The Event

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ESC Congress 2026

28 August 2026 15:15 CET
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ESC 365 is supported by

novartis Explore sponsored resources
novo-nordisk Explore sponsored resources