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

Back

ESC Premium Access

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

Congress Presentation

About the speaker

Mr Daniel Perez Vicencio

Edinburgh (United Kingdom of Great Britain & Northern Ireland)

0 follower

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)

Access the full session

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

Access

Video

About the event

ESC Congress 2024

30 August - 2 September 2024

Sessions Presentations

Congress

ESC 365 is supported by

Explore sponsored resources