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
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/915a5d2798964ce88bcbdfa8e223f3ce
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spelling	oai:doaj.org-article:915a5d2798964ce88bcbdfa8e223f3ce2021-12-02T13:20:02ZCombined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea10.1038/s41598-021-84003-42045-2322https://doaj.org/article/915a5d2798964ce88bcbdfa8e223f3ce2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84003-4https://doaj.org/toc/2045-2322Abstract 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.Eun-Yeol MaJeong-Whun KimYoungmin LeeSung-Woo ChoHeeyoung KimJae Kyoung KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Eun-Yeol Ma Jeong-Whun Kim Youngmin Lee Sung-Woo Cho Heeyoung Kim Jae Kyoung Kim Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea
description	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-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.
format	article
author	Eun-Yeol Ma Jeong-Whun Kim Youngmin Lee Sung-Woo Cho Heeyoung Kim Jae Kyoung Kim
author_facet	Eun-Yeol Ma Jeong-Whun Kim Youngmin Lee Sung-Woo Cho Heeyoung Kim Jae Kyoung Kim
author_sort	Eun-Yeol Ma
title	Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea
title_short	Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea
title_full	Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea
title_fullStr	Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea
title_full_unstemmed	Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea
title_sort	combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/915a5d2798964ce88bcbdfa8e223f3ce
work_keys_str_mv	AT eunyeolma combinedunsupervisedsupervisedmachinelearningforphenotypingcomplexdiseaseswithitsapplicationtoobstructivesleepapnea AT jeongwhunkim combinedunsupervisedsupervisedmachinelearningforphenotypingcomplexdiseaseswithitsapplicationtoobstructivesleepapnea AT youngminlee combinedunsupervisedsupervisedmachinelearningforphenotypingcomplexdiseaseswithitsapplicationtoobstructivesleepapnea AT sungwoocho combinedunsupervisedsupervisedmachinelearningforphenotypingcomplexdiseaseswithitsapplicationtoobstructivesleepapnea AT heeyoungkim combinedunsupervisedsupervisedmachinelearningforphenotypingcomplexdiseaseswithitsapplicationtoobstructivesleepapnea AT jaekyoungkim combinedunsupervisedsupervisedmachinelearningforphenotypingcomplexdiseaseswithitsapplicationtoobstructivesleepapnea
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Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea

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