Use of artificial intelligence to predict outcomes in mild aortic valve stenosis
Machine learning models can be used in patients with mild AS to identify those at high risk of disease progression and mortality. Implementation of such models may facilitate real-time, patient-specific follow-up recommendations.
Authors
- Jordi Sanchez Dahl (ORCID: https://orcid.org/0000-0002-8345-204X)
- Alon Lanyado (ORCID: https://orcid.org/0000-0002-2159-4659)
- Nader Al-Shakarchi (ORCID: https://orcid.org/0000-0003-1496-8103)
- Patricia A. Pellikka (ORCID: https://orcid.org/0000-0001-6800-3521)
- Ratnasari Padang (ORCID: https://orcid.org/0000-0002-1233-3924)
- Vuyisile T. Nkomo (ORCID: https://orcid.org/0000-0001-6095-5579)
- John I. Jackson
- Christopher G. Scott (ORCID: https://orcid.org/0000-0003-1340-0647)
- Raghav Julakanti (ORCID: https://orcid.org/0000-0002-0143-7517)
- Coby Metzger
Institutions
- Mayo Clinic (US)
- Mayo Clinic in Arizona (US)
- Roche (Israel) (IL)
Publication Details
- Journal
- European Heart Journal - Digital Health
- Published
- 2024-11-11
- DOI
- https://doi.org/10.1093/ehjdh/ztae085
- Citations
- 3
- Primary Topic
- Cardiac Valve Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- Mayo Clinic