Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients

Background: Remodeling due to myocardial infarction (MI) significantly increases patient arrhythmic risk. Simulations using patient-specific models have shown promise in predicting personalized risk for arrhythmia. However, these are computationally- and time- intensive, hindering translation to cli...

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Detalles Bibliográficos
Autores principales: Maleckar, Mary M., Myklebust, Lena, Uv, Julie, Florvaag, Per Magne, Strøm, Vilde, Glinge, Charlotte, Jabbari, Reza, Vejlstrup, Niels, Engstrøm, Thomas, Ahtarovski, Kiril, Jespersen, Thomas, Tfelt-Hansen, Jacob, Naumova, Valeriya, Arevalo, Hermenegild
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Physiology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606551/
https://www.ncbi.nlm.nih.gov/pubmed/34819872
http://dx.doi.org/10.3389/fphys.2021.745349

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606551/
https://www.ncbi.nlm.nih.gov/pubmed/34819872
http://dx.doi.org/10.3389/fphys.2021.745349

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