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Predicting pneumonia death in patients with heart failure using explainable machine learning model in a territory-wide electronic health record cohort

29 August 2026 (14:15 - 15:00)
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Congress Presentation Part of: Artificial intelligence in heart failure: advancing early detection, risk stratification, and patient management Machine Learning ESC2026 Premium Access ESC Congress 2026 European Society of Cardiology

About the speaker

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Ms Ami Matsumoto

(Hong Kong)

7 More presentations in this session

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Hierarchical anatomical learning improves detection of heart failure signs on chest X-rays
Mr J. Moon (Daejeon, KR)
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CARE-HEART: AI-driven prediction in heart failure for timely advance care planning
Ms M. Tans (Amsterdam, NL)
Abstract Slides
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Multimodal transformer decoder for the early detection of clinical deterioration in heart failure: Insights from temporal lead-time analysis
Mr J. Moon (Daejeon, KR)
Abstract Slides
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Development and evaluation of an AI-ECG model for incident heart failure risk prediction: a cross-continent study
Mr X. An (Sheffield, GB)
Abstract Slides Video
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CIED-derived sleep apnea burden and risk of heart failure hospitalization: a registry-based analysis
Associate Professor T. Bekfani (Magdeburg, DE)
Abstract Slides Video

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Artificial intelligence in heart failure: advancing early detection, risk stratification, and patient management

The Event

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ESC Congress 2026

29 August 2026 14:15 CET
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ESC 365 is supported by

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