Artificial intelligence to predict biomarkers for new-onset atrial fibrillation after coronary artery bypass grafting
Machine learning models demonstrate encouraging results in identifying risk factors for many entities. It is of utmost importance to establish a ranking among risk factors and determine threshold values to support clinicians in decision making. This is our first experience with machine learning in this patient group after cardiac surgery. Further studies are warranted to confirm these data.
Authors
- Mesut Yılmaz (ORCID: https://orcid.org/0000-0001-8799-3452)
- Güzin Aykal (ORCID: https://orcid.org/0000-0002-2413-2695)
- Okan Oral (ORCID: https://orcid.org/0000-0002-6302-4574)
- Birkan Akbulut (ORCID: https://orcid.org/0000-0002-4046-7268)
- Mustafa Çakır (ORCID: https://orcid.org/0000-0002-1794-9242)
- Metin Sarıkaya (ORCID: https://orcid.org/0009-0009-6401-0041)
Institutions
- Akdeniz University (TR)
- Antalya Eğitim ve Araştırma Hastanesi (TR)
- İskenderun Technical University (TR)
Publication Details
- Journal
- Turkish Journal of Thoracic and Cardiovascular Surgery
- Published
- 2025-04-01
- DOI
- https://doi.org/10.5606/tgkdc.dergisi.2025.27304
- Citations
- 5
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 6.94