Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia

OBJECTIVE: Unsupervised machine learning approaches hold promise for large-scale clinical data. However, the heterogeneity of clinical data raises new methodological challenges in feature selection, choosing a distance metric that captures biological meaning, and visualization. We hypothesized that...

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Detalles Bibliográficos
Autores principales: Coombes, Caitlin E, Abrams, Zachary B, Li, Suli, Abruzzo, Lynne V, Coombes, Kevin R
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Research and Applications
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647286/
https://www.ncbi.nlm.nih.gov/pubmed/32483590
http://dx.doi.org/10.1093/jamia/ocaa060

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647286/
https://www.ncbi.nlm.nih.gov/pubmed/32483590
http://dx.doi.org/10.1093/jamia/ocaa060

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