Cargando…
Application of a machine learning algorithm for detection of atrial fibrillation in secondary care
Atrial fibrillation (AF) is the most common sustained heart arrhythmia and significantly increases risk of stroke. Opportunistic AF testing in high-risk patients typically requires frequent electrocardiogram tests to capture the arrhythmia. Risk-prediction algorithms may help to more accurately iden...
Autores principales: | Pollock, Kevin G., Sekelj, Sara, Johnston, Ellie, Sandler, Belinda, Hill, Nathan R., Ng, Fu Siong, Khan, Sadia, Nassar, Ayman, Farooqui, Usman |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8164133/ https://www.ncbi.nlm.nih.gov/pubmed/34095444 http://dx.doi.org/10.1016/j.ijcha.2020.100674 |
Ejemplares similares
-
A Systematic Review of Network Meta-Analyses and Real-World Evidence Comparing Apixaban and Rivaroxaban in Nonvalvular Atrial Fibrillation
por: Hill, Nathan R., et al.
Publicado: (2020) -
Is machine learning the future for atrial fibrillation screening?
por: Sivanandarajah, Pavidra, et al.
Publicado: (2022) -
Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial
por: Hill, Nathan R., et al.
Publicado: (2020) -
Identification of undiagnosed atrial fibrillation using a machine learning risk-prediction algorithm and diagnostic testing (PULsE-AI) in primary care: a multi-centre randomized controlled trial in England
por: Hill, Nathan R, et al.
Publicado: (2022) -
Predicting atrial fibrillation in primary care using machine learning
por: Hill, Nathan R., et al.
Publicado: (2019)