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Explainable machine learning and multi-modal data for predicting readmission in patients with heart failure

2 September 2024 (11:00 - 11:50)
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Congress Presentation Part of: Models to predict unfavourable outcomes in heart failure Epidemiology, Prognosis, Outcome ESC Professional Premium Access ESC Congress 2024 European Society of Cardiology

About the speaker

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Ms Rachel Chae

(United States of America)

8 More presentations in this session

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Risk prediction models for incident heart failure: a systematic review and meta-analysis
Mr J. Navarro (Toronto, CA)
Abstract Slides Video
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A flexible, calibrated transformer-based risk model for prediction of clinical outcomes in patients with heart failure; an open cohort study using linked UK EHR
Doctor S. Rao (Oxford, GB)
Abstract Slides Video
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Prognostic value of reticulocyte production ability in patients with chronic heart failure
Doctor M. Nakao (Sapporo, JP)
Abstract Slides Video
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Prognostic implications of genotype findings in non-ischemic dilated cardiomyopathy: a network meta-analysis
Doctor V. Anastasiou (Thessaloniki, GR)
Abstract Slides Video
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Development and validation of a predicting model for risk of mortality in heart failure patients with mildly reduced ejection fraction post discharge
Professor Y. Zhu (Xiangtan, CN)
Abstract Slides Video

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Models to predict unfavourable outcomes in heart failure

The Event

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ESC Congress 2024

2 September 2024 11:00 CET
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ESC 365 is supported by

novartis Explore sponsored resources