Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.16/2339 |
Resumo: | Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapid-eye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing 'ideas' and promising candidates for future diagnostic classifications. |
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Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learningHumansNarcolepsyPolysomnographyROC CurveRare DiseasesSleep LatencySleep, REMStochastic ProcessesModels, BiologicalSupervised Machine LearningNarcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapid-eye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing 'ideas' and promising candidates for future diagnostic classifications.The EU-NN database is financed by the EU-NN. The EU-NN has received financial support from UCB Pharma Brussels for developing the EU-NN database.Nature ResearchRepositório Científico do Centro Hospitalar Universitário de Santo AntónioZhang, Z.Mayer, G.Dauvilliers, Y.Plazzi, G.Pizza, F.Fronczek, R.Santamaria, J.Partinen, M.Overeem, S.Peraita-Adrados, R.Silva, A.Sonka, K.Rio-Villegas, R.Heinzer, R.Wierzbicka, A.Young, P.Högl, B.Bassetti, C.Manconi, M.Feketeova, E.Mathis, J.Paiva, T.Canellas, F.Lecendreux, M.Baumann, C.Barateau, L.Pesenti, C.Antelmi, E.Gaig, C.Iranzo, A.Lillo-Triguero, L.Medrano-Martínez, P.Haba-Rubio, J.Gorban, C.Luca, G.Lammers, G.Khatami, R.2020-03-21T17:40:47Z2018-07-132018-07-13T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.16/2339engZhang Z, Mayer G, Dauvilliers Y, et al. Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning. Sci Rep. 2018;8(1):10628. Published 2018 Jul 13.2045-232210.1038/s41598-018-28840-winfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-10-20T11:00:22Zoai:repositorio.chporto.pt:10400.16/2339Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:38:33.147870Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning |
title |
Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning |
spellingShingle |
Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning Zhang, Z. Humans Narcolepsy Polysomnography ROC Curve Rare Diseases Sleep Latency Sleep, REM Stochastic Processes Models, Biological Supervised Machine Learning |
title_short |
Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning |
title_full |
Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning |
title_fullStr |
Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning |
title_full_unstemmed |
Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning |
title_sort |
Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning |
author |
Zhang, Z. |
author_facet |
Zhang, Z. Mayer, G. Dauvilliers, Y. Plazzi, G. Pizza, F. Fronczek, R. Santamaria, J. Partinen, M. Overeem, S. Peraita-Adrados, R. Silva, A. Sonka, K. Rio-Villegas, R. Heinzer, R. Wierzbicka, A. Young, P. Högl, B. Bassetti, C. Manconi, M. Feketeova, E. Mathis, J. Paiva, T. Canellas, F. Lecendreux, M. Baumann, C. Barateau, L. Pesenti, C. Antelmi, E. Gaig, C. Iranzo, A. Lillo-Triguero, L. Medrano-Martínez, P. Haba-Rubio, J. Gorban, C. Luca, G. Lammers, G. Khatami, R. |
author_role |
author |
author2 |
Mayer, G. Dauvilliers, Y. Plazzi, G. Pizza, F. Fronczek, R. Santamaria, J. Partinen, M. Overeem, S. Peraita-Adrados, R. Silva, A. Sonka, K. Rio-Villegas, R. Heinzer, R. Wierzbicka, A. Young, P. Högl, B. Bassetti, C. Manconi, M. Feketeova, E. Mathis, J. Paiva, T. Canellas, F. Lecendreux, M. Baumann, C. Barateau, L. Pesenti, C. Antelmi, E. Gaig, C. Iranzo, A. Lillo-Triguero, L. Medrano-Martínez, P. Haba-Rubio, J. Gorban, C. Luca, G. Lammers, G. Khatami, R. |
author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Centro Hospitalar Universitário de Santo António |
dc.contributor.author.fl_str_mv |
Zhang, Z. Mayer, G. Dauvilliers, Y. Plazzi, G. Pizza, F. Fronczek, R. Santamaria, J. Partinen, M. Overeem, S. Peraita-Adrados, R. Silva, A. Sonka, K. Rio-Villegas, R. Heinzer, R. Wierzbicka, A. Young, P. Högl, B. Bassetti, C. Manconi, M. Feketeova, E. Mathis, J. Paiva, T. Canellas, F. Lecendreux, M. Baumann, C. Barateau, L. Pesenti, C. Antelmi, E. Gaig, C. Iranzo, A. Lillo-Triguero, L. Medrano-Martínez, P. Haba-Rubio, J. Gorban, C. Luca, G. Lammers, G. Khatami, R. |
dc.subject.por.fl_str_mv |
Humans Narcolepsy Polysomnography ROC Curve Rare Diseases Sleep Latency Sleep, REM Stochastic Processes Models, Biological Supervised Machine Learning |
topic |
Humans Narcolepsy Polysomnography ROC Curve Rare Diseases Sleep Latency Sleep, REM Stochastic Processes Models, Biological Supervised Machine Learning |
description |
Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapid-eye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing 'ideas' and promising candidates for future diagnostic classifications. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-07-13 2018-07-13T00:00:00Z 2020-03-21T17:40:47Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.16/2339 |
url |
http://hdl.handle.net/10400.16/2339 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Zhang Z, Mayer G, Dauvilliers Y, et al. Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning. Sci Rep. 2018;8(1):10628. Published 2018 Jul 13. 2045-2322 10.1038/s41598-018-28840-w |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Nature Research |
publisher.none.fl_str_mv |
Nature Research |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799133646420967424 |