ESC 365 - Predicting postoperative atrial fibrillation after cardiac surgery using machine learning

ESC2025 Premium Access

Predicting postoperative atrial fibrillation after cardiac surgery using machine learning

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

Topic: Machine Learning

Speaker: Doctor I. Kim (Seoul, KR)

Event: ESC Congress 2025

Congress Presentation

Add to my library

About the speaker

Doctor In Jung Kim

Seoul National University Hospital, Seoul (Korea (Republic of))

0 follower

6 more presentations in this session

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)

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

Speaker: Professor A. Banerjee (London, GB)

Access the full session

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

Speakers: Doctor I. Kim, Doctor L. Riehle, Doctor V. Goel, Doctor M. Kelshiker, Mr R. Cavarra...

About the event

ESC Congress 2025

29 August - 1 September 2025

Sessions Presentations

Congress

ESC 365 is supported by

bristol-myers-squibb

Explore sponsored resources

novartis

Explore sponsored resources