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 clusters and key phenotypic features have been subjectively selected, reducing the reliability of the phenotyping results. Here, to minimize such subjective decisions for highly confident phenotyping, we develop a multimetric phenotyping framework by combining supervised and unsupervised machine learning. This clusters 2277 OSA patients to six phenotypes based on their multidimensional polysomnography (PSG) data. Importantly, these new phenotypes show statistically different comorbidity development for OSA-

SUBMITTER: Ma EY 

PROVIDER: S-EPMC7904925 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature