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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:...
| Autores principales: | , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2021
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| Materias: | |
| 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 |