Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea

Abstract 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 clu...

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Autores principales: Eun-Yeol Ma, Jeong-Whun Kim, Youngmin Lee, Sung-Woo Cho, Heeyoung Kim, Jae Kyoung Kim
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/915a5d2798964ce88bcbdfa8e223f3ce
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