Multi‐modality machine learning approach for risk stratification in heart failure with left ventricular ejection fraction ≤ 45%

AIMS: Heart failure (HF) involves complex remodelling leading to electrical and mechanical dysfunction. We hypothesized that machine learning approaches incorporating data obtained from different investigative modalities including atrial and ventricular measurements from electrocardiography and echo...

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Detalles Bibliográficos
Autores principales: Tse, Gary, Zhou, Jiandong, Woo, Samuel Won Dong, Ko, Ching Ho, Lai, Rachel Wing Chuen, Liu, Tong, Liu, Yingzhi, Leung, Keith Sai Kit, Li, Andrew, Lee, Sharen, Li, Ka Hou Christien, Lakhani, Ishan, Zhang, Qingpeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Original Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7754744/
https://www.ncbi.nlm.nih.gov/pubmed/33094925
http://dx.doi.org/10.1002/ehf2.12929

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7754744/
https://www.ncbi.nlm.nih.gov/pubmed/33094925
http://dx.doi.org/10.1002/ehf2.12929

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