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

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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-related cardio-neuro-metabolic diseases, unlike the conventional single-metric apnea–hypopnea index-based phenotypes. Furthermore, the key features of highly comorbid phenotypes were identified through supervised learning rather than subjective choice. These results can also be used to automatically phenotype new patients and predict their comorbidity risks solely based on their PSG data. The phenotyping framework based on the combination of unsupervised and supervised machine learning methods can also be applied to other complex, heterogeneous diseases for phenotyping patients and identifying important features for high-risk phenotypes.
Publisher
NATURE PUBLISHING GROUP
Issue Date
2021-02
Language
English
Article Type
Article
Citation

SCIENTIFIC REPORTS, v.11, no.1

ISSN
2045-2322
DOI
10.1038/s41598-021-84003-4
URI
http://hdl.handle.net/10203/281437
Appears in Collection
IE-Journal Papers(저널논문)MA-Journal Papers(저널논문)
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