Machine learning-based prediction model for recurrence after radiofrequency catheter ablation in patients with atrial fibrillation
Machine learning-based models show modest but promising performance for assessing AF recurrence risk after RFCA using routine clinical data. While requiring extensive external validation before clinical application, these models highlight the potential of ML to inform future risk stratification and guide personalized follow-up strategies.
Authors
- Tianwei Zhang (ORCID: https://orcid.org/0000-0001-6777-1668)
- Ling Nie (ORCID: https://orcid.org/0000-0002-7878-2001)
- Meng Liu (ORCID: https://orcid.org/0000-0002-2934-7978)
- Yanbo Chen (ORCID: https://orcid.org/0000-0003-4210-0598)
- Wen-Hua Wang (ORCID: https://orcid.org/0000-0002-3606-0326)
- Yujie Wang
- Xuefu Han
- Shujie Zhang (ORCID: https://orcid.org/0000-0003-3764-059X)
- Wenjiu Feng (ORCID: https://orcid.org/0000-0003-1157-8302)
Institutions
- Qingdao University (CN)
- Weifang Medical University (CN)
- Weifang People's Hospital (CN)
- Affiliated Hospital of Qingdao University (CN)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2025-08-08
- DOI
- https://doi.org/10.3389/fcvm.2025.1642409
- Citations
- 2
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 2.78