Utilizing 12-lead electrocardiogram and machine learning to retrospectively estimate and prospectively predict atrial fibrillation and stroke risk
The CNN models were effective for estimating the past and future risk of AF by analyzing 12-lead ECG. Patients with predicted AF had a higher risk of developing stroke, hospitalization for HF, MI, and death. By using this AF prediction model, physicians may be able to identify patients who should be screened for AF and taking action to prevent stroke and manage cardiovascular risk.
Authors
- Chung‐Chuan Chou (ORCID: https://orcid.org/0000-0002-9389-6525)
- Chang‐Fu Kuo (ORCID: https://orcid.org/0000-0002-9770-5730)
- Hung-Ta Wo (ORCID: https://orcid.org/0000-0003-4876-0714)
- Ching-Heng Lin (ORCID: https://orcid.org/0000-0002-8042-5484)
- Wen-Chen Lee
- Hao-Tien Liu (ORCID: https://orcid.org/0000-0003-0546-4181)
- Jung‐Sheng Chen (ORCID: https://orcid.org/0000-0003-3187-9479)
- Zhi‐Yong Liu (ORCID: https://orcid.org/0000-0003-2123-7923)
- Ming-Shien Wen
- Chun‐Chieh Wang
- P. Tung
- Po-Cheng Chang
- Yu-Chang Huang
Institutions
- Chang Gung University (TW)
- Chang Gung Memorial Hospital (TW)
- Linkou Chang Gung Memorial Hospital (TW)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2025-02-21
- DOI
- https://doi.org/10.1016/j.compbiomed.2025.109871
- Citations
- 3
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 4.16
Funders
- Ministry of Science and Technology, Taiwan