A Phenotyping of Diastolic Function by Machine Learning Improves Prediction of Clinical Outcomes in Heart Failure

Background: Discriminating between different patterns of diastolic dysfunction in heart failure (HF) is still challenging. We tested the hypothesis that an unsupervised machine learning algorithm would detect heterogeneity in diastolic function and improve risk stratification compared with recommend...

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Detalles Bibliográficos
Autores principales: Kameshima, Haruka, Uejima, Tokuhisa, Fraser, Alan G., Takahashi, Lisa, Cho, Junyi, Suzuki, Shinya, Kato, Yuko, Yajima, Junji, Yamashita, Takeshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Cardiovascular Medicine
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733156/
https://www.ncbi.nlm.nih.gov/pubmed/35004877
http://dx.doi.org/10.3389/fcvm.2021.755109

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733156/
https://www.ncbi.nlm.nih.gov/pubmed/35004877
http://dx.doi.org/10.3389/fcvm.2021.755109

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