Cargando…
The Athlete’s Heart and Machine Learning: A Review of Current Implementations and Gaps for Future Research
Background: Intense training exercise regimes cause physiological changes within the heart to help cope with the increased stress, known as the “athlete’s heart”. These changes can mask pathological changes, making them harder to diagnose and increasing the risk of an adverse cardiac outcome. Aim: T...
Autores principales: | Bellfield, Ryan A. A., Ortega-Martorell, Sandra, Lip, Gregory Y. H., Oxborough, David, Olier, Ivan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9692556/ https://www.ncbi.nlm.nih.gov/pubmed/36354781 http://dx.doi.org/10.3390/jcdd9110382 |
Ejemplares similares
-
How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management
por: Olier, Ivan, et al.
Publicado: (2021) -
Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks
por: Ortega-Martorell, Sandra, et al.
Publicado: (2023) -
New use for an old drug: Metformin and atrial fibrillation
por: Vinciguerra, Manlio, et al.
Publicado: (2022) -
Association between metabolically healthy obesity and risk of atrial fibrillation: taking physical activity into consideration
por: Wang, Ruoting, et al.
Publicado: (2022) -
Enhanced survival prediction using explainable artificial intelligence in heart transplantation
por: Lisboa, Paulo J. G., et al.
Publicado: (2022)