Back

Clinical applications of machine learning for prediction of incident atrial fibrillation from the general population: a nationwide cohort study

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

About the speaker

Photo
Associate Professor In-Soo Kim

Gangnam Severance Hospital, Seoul (Korea (Republic of))

6 More presentations in this session

Presentation thumbnail
Machine learning for phenotyping and risk prediction in cardiovascular diseases: a systematic review
Associate Professor A. Banerjee (London, GB)
Abstract
Presentation thumbnail
Finding predictors and causes of cardiac surgery ICU readmission using machine learning and causal inference
Mr J. Lee (Baltimore, US)
Abstract Slides
Presentation thumbnail
An evaluation of the use of propensity scores in cardiovascular literature: a systematic review and recommendations
Ms J. Rouette (Montreal, CA)
Abstract Slides
Presentation thumbnail
Balancing risk and benefit in patients with atrial fibrillation: the GARFIELD-AF risk score
Professor K. Fox (Edinburgh, GB)
Abstract Slides
Presentation thumbnail
External validation of the ACEF II operative risk model in a cardiac surgery population: an interim evaluation
Doctor M. Georgievska (Skopje, MK)
Abstract Slides

Access the full session

Session thumbnail
New approaches to risk prediction

The Event

Event poster

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