Machine learning to improve the performance of natriuretic peptides across age groups for the diagnosis of acute heart failure

30 August 2024 (09:40 - 09:50)

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Mr Daniel Perez Vicencio

Edinburgh (United Kingdom of Great Britain & Northern Ireland)

4 More presentations in this session

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Development and validation of a predicting model for risk of rehospitalization within 30 days in heart failure patients with preserved ejection fraction

Professor Y. Zhu (Xiangtan, CN)

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Enhancing the selection of heart failure patients for invasive procedures: how machine learning can predict the diagnostic yield of endomyocardial biopsy

Doctor C. Basile (Stockholm, SE)

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High prevalence of cardiotropic viruses in endomyocardial biopsies from patients with inflammatory cardiomyopathy demonstrated by a novel targeted NGS approach

Doctor C. Baumeier (Berlin, DE)

Abstract Slides Video

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Predicting heart failure in cancer survivors: use of polygenic risk vs clinical score

Doctor C. Soh (Melbourne, AU)

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Get out your crystal ball: predicting heart failure events using models

The Event

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ESC Congress 2024

30 August 2024 09:40 CET

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ESC 365 is supported by

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