Machine-learning informed subtypes of heart failure and their palliative care trajectories: a population-based cohort study

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Machine-learning informed subtypes of heart failure and their palliative care trajectories: a population-based cohort study

Session: Artificial intelligence in action: implications for atrial fibrillation and heart failure

Topic: Machine Learning

Speaker: Professor A. Banerjee (London, GB)

Event: ESC Congress 2025

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About the speaker

Professor Amitava Banerjee

University College London, London (United Kingdom of Great Britain & Northern Ireland)

6 presentations

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6 more presentations in this session

Predicting postoperative atrial fibrillation after cardiac surgery using machine learning

Speaker: Doctor I. Kim (Seoul, KR)

AI-driven voice analysis for early fluid overload detection in heart failure: preliminary results from the DHZC cohort of the VAMP-HF study

Speaker: Doctor L. Riehle (Berlin, DE)

Machine learning modelling to predict heart failure readmission following emergency department presentation: a systems integration opportunity

Speaker: Doctor V. Goel (Melbourne, AU)

Detection of heart failure with an artificial intelligence-enabled stethoscope in primary care: a multicentre prospective external validation study

Speaker: Doctor M. Kelshiker (London, GB)

Explainable machine learning models to improve prediction of incident stroke in atrial fibrillation patients using health records, medical imaging and ECG derived metrics

Speaker: Mr R. Cavarra (London, GB)

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Artificial intelligence in action: implications for atrial fibrillation and heart failure

Speakers: Professor A. Banerjee, Doctor I. Kim, Doctor L. Riehle, Doctor V. Goel, Doctor M. Kelshiker...

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ESC Congress 2025

29 August - 1 September 2025

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