Cargando…
How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management
There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availabi...
Autores principales: | Olier, Ivan, Ortega-Martorell, Sandra, Pieroni, Mark, Lip, Gregory Y H |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8477792/ https://www.ncbi.nlm.nih.gov/pubmed/33982064 http://dx.doi.org/10.1093/cvr/cvab169 |
Ejemplares similares
-
New use for an old drug: Metformin and atrial fibrillation
por: Vinciguerra, Manlio, et al.
Publicado: (2022) -
The Athlete’s Heart and Machine Learning: A Review of Current Implementations and Gaps for Future Research
por: Bellfield, Ryan A. A., et al.
Publicado: (2022) -
Optimizing indices of atrial fibrillation susceptibility and burden to
evaluate atrial fibrillation severity, risk and outcomes
por: Boriani, Giuseppe, et al.
Publicado: (2021) -
Association between metabolically healthy obesity and risk of atrial fibrillation: taking physical activity into consideration
por: Wang, Ruoting, et al.
Publicado: (2022) -
Development of a Risk Prediction Model for New Episodes of Atrial Fibrillation in Medical-Surgical Critically Ill Patients Using the AmsterdamUMCdb
por: Ortega-Martorell, Sandra, et al.
Publicado: (2022)