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Machine learning for prediction of incident heart failure and heart failure hospitalisation: multi-cohort development, validation and association with clinical phenotypes

31 August 2024 (10:20 - 10:30)
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Congress Presentation Part of: Innovations in cardiac health prediction: integrating deep learning with multimodal data analysis Artificial Intelligence ESC Professional Premium Access ESC Congress 2024 European Society of Cardiology

About the speaker

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Doctor Yoko Nakao

Kyoto University, Kyoto (Japan)

5 More presentations in this session

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Mobile single-lead ECG atrial fibrillation prediction enhancement integrated by standard ECG algorithm with Deep learning model
Associate Professor K. Lee (Gwangju, KR)
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ECGWiz: a unified and generalizable AI model for comprehensive ECG diagnosis
Mr I. Vatsaraj (Baltimore, US)
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Multi-modality deep learning prediction of heart age: insights from UK-Biobank.
Doctor G. Siontis (Bern, CH)
Abstract Slides Video
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Detection and risk stratification of cardiac amyloidosis patients by integration of imaging and non-imaging data using a machine learning approach
Doctor C. Spielvogel (Vienna, AT)
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Standardization of transthoracic impedance values for estimating heart failure and its utility
Doctor D. Nose (Fukuoka, JP)
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Innovations in cardiac health prediction: integrating deep learning with multimodal data analysis
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The Event

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ESC Congress 2024

31 August 2024 10:20 CET
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ESC 365 is supported by

novartis Explore sponsored resources