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Interpretable dynamic graph learning for low-frequency cohorts: mapping pseudotime windows to CKM network dynamics

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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Doctor Tong Liu

2nd Hospital of Tianjin Medical University, Tianjin (China)

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)
Abstract Slides
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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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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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Precision screening for cardiac amyloidosis using explainable AI: a threshold-optimised random forest pipeline with SHAP-based interpretability
Doctor M. Brandt (Mainz, DE) Mr J. Tuennermann (DE)
Abstract Slides

Access the full session

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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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