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Machine learning for phenotyping and risk prediction in cardiovascular diseases: a systematic review

3 September 2019 (12:40 - 13:30)
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Congress Presentation Part of: New approaches to risk prediction Big Data Analysis ESC Premium Access ESC Congress 2019

About the speaker

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Professor Amitava Banerjee

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

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Finding predictors and causes of cardiac surgery ICU readmission using machine learning and causal inference
Mr J. Lee (Baltimore, US)
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An evaluation of the use of propensity scores in cardiovascular literature: a systematic review and recommendations
Ms J. Rouette (Montreal, CA)
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Balancing risk and benefit in patients with atrial fibrillation: the GARFIELD-AF risk score
Professor K. Fox (Edinburgh, GB)
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External validation of the ACEF II operative risk model in a cardiac surgery population: an interim evaluation
Doctor M. Georgievska (Skopje, MK)
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Clinical applications of machine learning for prediction of incident atrial fibrillation from the general population: a nationwide cohort study
Doctor I. Kim (Seoul, KR)
Abstract

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New approaches to risk prediction

The Event

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ESC Congress 2019

3 September 2019 12:40 CET

ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources