Deep significance clustering: a novel approach for identifying risk-stratified and predictive patient subgroups

OBJECTIVE: Deep significance clustering (DICE) is a self-supervised learning framework. DICE identifies clinically similar and risk-stratified subgroups that neither unsupervised clustering algorithms nor supervised risk prediction algorithms alone are guaranteed to generate. MATERIALS AND METHODS:...

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Detalles Bibliográficos
Autores principales: Huang, Yufang, Liu, Yifan, Steel, Peter A D, Axsom, Kelly M, Lee, John R, Tummalapalli, Sri Lekha, Wang, Fei, Pathak, Jyotishman, Subramanian, Lakshminarayanan, Zhang, Yiye
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Research and Applications
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8500061/
https://www.ncbi.nlm.nih.gov/pubmed/34571540
http://dx.doi.org/10.1093/jamia/ocab203

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8500061/
https://www.ncbi.nlm.nih.gov/pubmed/34571540
http://dx.doi.org/10.1093/jamia/ocab203

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