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

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

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

Topic: Biomarkers

Speaker: Mr D. Perez Vicencio (Edinburgh, GB)

Event: ESC Congress 2024

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Mr Daniel Perez Vicencio

Edinburgh (United Kingdom of Great Britain & Northern Ireland)

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4 more presentations in this session

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

Speaker: Professor Y. Zhu (Xiangtan, CN)

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

Speaker: Doctor C. Basile (Naples, IT)

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

Speaker: Doctor C. Baumeier (Berlin, DE)

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

Speaker: Doctor C. Soh (Melbourne, AU)

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

Speakers: Mr D. Perez Vicencio, Professor Y. Zhu, Doctor C. Basile, Doctor C. Baumeier, Doctor C. Soh

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ESC Congress 2024

30 August - 2 September 2024

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