Residual risk prediction in anticoagulated patients with atrial fibrillation using machine learning: A report from the GLORIA‐AF registry phase II/III
ML algorithms can improve the prediction of residual risk of anticoagulated AF patients compared to clinical risk factor-based scores.
Authors
- Iván Olier (ORCID: https://orcid.org/0000-0002-5679-7501)
- Kui Hong
- Steven Ho Man Lam (ORCID: https://orcid.org/0000-0003-2147-0995)
- Bi Huang (ORCID: https://orcid.org/0000-0002-1763-8452)
- Hironori Ishiguchi (ORCID: https://orcid.org/0000-0003-4597-7862)
- Sandra Ortega‐Martorell (ORCID: https://orcid.org/0000-0001-9927-3209)
- Yang Chen (ORCID: https://orcid.org/0000-0002-2808-6286)
- Yang Liu (ORCID: https://orcid.org/0000-0001-8233-9291)
- Menno V. Huisman
- Gregory Y. H. Lip
Institutions
- Nanchang University (CN)
- University of Liverpool (GB)
- Yamaguchi University (JP)
- Leiden University Medical Center (NL)
- Liverpool Heart and Chest Hospital (GB)
- Second Affiliated Hospital of Nanchang University (CN)
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Liverpool John Moores University (GB)
- Chongqing Medical University (CN)
- Aalborg University (DK)
Publication Details
- Journal
- European Journal of Clinical Investigation
- Published
- 2024-12-11
- DOI
- https://doi.org/10.1111/eci.14371
- Citations
- 17
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 6.83
Funders
- Nanchang University