Machine learning for multidimensional response and survival after cardiac resynchronization therapy using features from cardiac magnetic resonance

BACKGROUND: Cardiac resynchronization therapy (CRT) response is complex, and better approaches are required to predict survival and need for advanced therapies. OBJECTIVE: The objective was to use machine learning to characterize multidimensional CRT response and its relationship with long-term surv...

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Detalles Bibliográficos
Autores principales: Bivona, Derek J., Tallavajhala, Srikar, Abdi, Mohamad, Oomen, Pim J.A., Gao, Xu, Malhotra, Rohit, Darby, Andrew E., Monfredi, Oliver J., Mangrum, J. Michael, Mason, Pamela K., Mazimba, Sula, Salerno, Michael, Kramer, Christopher M., Epstein, Frederick H., Holmes, Jeffrey W., Bilchick, Kenneth C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Clinical
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9626744/
https://www.ncbi.nlm.nih.gov/pubmed/36340495
http://dx.doi.org/10.1016/j.hroo.2022.06.005

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9626744/
https://www.ncbi.nlm.nih.gov/pubmed/36340495
http://dx.doi.org/10.1016/j.hroo.2022.06.005

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