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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Detalles Bibliográficos
Autores principales: Ma, Eun-Yeol, Kim, Jeong-Whun, Lee, Youngmin, Cho, Sung-Woo, Kim, Heeyoung, Kim, Jae Kyoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7904925/
https://www.ncbi.nlm.nih.gov/pubmed/33627761
http://dx.doi.org/10.1038/s41598-021-84003-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7904925/
https://www.ncbi.nlm.nih.gov/pubmed/33627761
http://dx.doi.org/10.1038/s41598-021-84003-4

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