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Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea
Unsupervised clustering models have been widely used for multimetric phenotyping of complex and heterogeneous diseases such as diabetes and obstructive sleep apnea (OSA) to more precisely characterize the disease beyond simplistic conventional diagnosis standards. However, the number of clusters and...
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| Publicado no: | Sci Rep |
|---|---|
| Main Authors: | , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Nature Publishing Group UK
2021
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7904925/ https://ncbi.nlm.nih.gov/pubmed/33627761 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-021-84003-4 |
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