Evaluating Machine Learning Models for Stroke Prognosis and Prediction in Atrial Fibrillation Patients: A Comprehensive Meta-Analysis
While ML models demonstrate potential for enhancing stroke prognosis and AF prediction, they have yet to meet the clinical standards required for widespread adoption. Future efforts should focus on refining these models and validating them across diverse populations to improve their clinical utility.
Authors
- Sonu Bhaskar (ORCID: https://orcid.org/0000-0002-9783-3628)
- Bill Goh
Institutions
- New South Wales Department of Health (AU)
- Liverpool Hospital (AU)
- UNSW Sydney (AU)
- National Cerebral and Cardiovascular Center (JP)
- Ingham Institute (AU)
- South Western Sydney Local Health District (AU)
Publication Details
- Journal
- Diagnostics
- Published
- 2024-10-26
- DOI
- https://doi.org/10.3390/diagnostics14212391
- Citations
- 7
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 2.81
Funders
- Australian Academy of Science
- Ministry of Education, Culture, Sports, Science and Technology
- Japan Society for the Promotion of Science