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Machine learning in critical care: the role of diabetes and age in acute coronary syndromes

31 August 2019 (00:00 - 00:00)
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Congress Presentation Part of: Poster Session 7 - Acute Coronary Syndromes - Risk stratification, machine learning Epidemiology, Prognosis, Outcome ESC Premium Access ESC Congress 2019

About the speaker

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Doctor Edina Cenko

University of Bologna, Bologna (Italy)

5 More presentations in this session

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Increasing the accuracy of a machine learning algorithm for stemi diagnosis by incorporating demographic variables
Doctor A. Frauenfelder (Miami, US)
Abstract Slides
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The continued proficiency of artificial intelligence for interpreting ekg: single lead ekg for stemi culprit lesion localization
Doctor S. Mehta (Miami, US)
Abstract Slides
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Can machine learning help us improve risk stratification of diabetic patients with acute coronary syndromes? The answer will blow your mind
Doctor J. Milner (Santa Maria da Feira, PT)
Abstract
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Can cardiologists rely on artificial intelligence to identify the culprit vessel in stemi?
Doctor D. Vieira (Miami, US)
Abstract Slides
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Physician vs machine: an innovative st-elevation myocardial infarction pathway through artificial intelligence
Doctor D. Vieira (Miami, US)
Abstract Slides

Access the full session

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Poster Session 7 - Acute Coronary Syndromes - Risk stratification, machine learning

The Event

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ESC Congress 2019

31 August - 4 September 2019

ESC 365 is supported by

novartis Explore sponsored resources

ESC 365 is supported by

novartis Explore sponsored resources