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Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning
Z. Zhang, G. Mayer, Y. Dauvilliers, G. Plazzi, F. Pizza, R. Fronczek, J. Santamaria, M. Partinen, S. Overeem, R. Peraita-Adrados, AM. da Silva, K. Sonka, RD. Rio-Villegas, R. Heinzer, A. Wierzbicka, P. Young, B. Högl, CL. Bassetti, M. Manconi, E....
Jazyk angličtina Země Velká Británie
Typ dokumentu časopisecké články, práce podpořená grantem
NLK
Directory of Open Access Journals
od 2011
Free Medical Journals
od 2011
Nature Open Access
od 2011-12-01
PubMed Central
od 2011
Europe PubMed Central
od 2011
ProQuest Central
od 2011-01-01
Open Access Digital Library
od 2011-01-01
Open Access Digital Library
od 2011-01-01
Health & Medicine (ProQuest)
od 2011-01-01
ROAD: Directory of Open Access Scholarly Resources
od 2011
- MeSH
- biologické modely * MeSH
- databáze faktografické statistika a číselné údaje MeSH
- datové soubory jako téma MeSH
- dospělí MeSH
- interpretace statistických dat MeSH
- lidé MeSH
- mladý dospělý MeSH
- narkolepsie klasifikace diagnóza patofyziologie MeSH
- polysomnografie statistika a číselné údaje MeSH
- řízené strojové učení * MeSH
- ROC křivka MeSH
- spánek REM fyziologie MeSH
- spánková latence fyziologie MeSH
- stochastické procesy MeSH
- vzácné nemoci klasifikace diagnóza patofyziologie MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
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.
Center for Investigation and Research in Sleep Lausanne University Hospital Lausanne Switzerland
Centre Neuchatelois de Psychiatrie Neuchatel Switzerland
Department of Clinical Neurophysiology Institute of Psychiatry and Neurology Warsaw Poland
Department of Neurology Inselspital Bern University Hospital and University of Bern Bern Switzerland
Department of Sleep Medicine and Neuromuscular Disorders University of Münster Münster Germany
Helsinki Sleep Clinic Vitalmed Research Center Helsinki Finland
Institute of Molecular Medicine Portugal Medical Faculty Lisbon University Lisbon Portugal
Neurology Department Hephata Klinik Schwalmstadt Germany
Neurology Department Sleep Disorders Clinic Medical University of Innsbruck Innsbruck Austria
Neurology Department University Hospital Zurich Zurich Switzerland
Unidad de Neurofisiología y Trastornos del Sueño Hospital Vithas Internacional Madrid Madrid Spain
Citace poskytuje Crossref.org
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- $a Zhang, Zhongxing $u Center for Sleep Medicine, Sleep Research and Epileptology, Klinik Barmelweid AG, Barmelweid, Switzerland.
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- $a Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning / $c Z. Zhang, G. Mayer, Y. Dauvilliers, G. Plazzi, F. Pizza, R. Fronczek, J. Santamaria, M. Partinen, S. Overeem, R. Peraita-Adrados, AM. da Silva, K. Sonka, RD. Rio-Villegas, R. Heinzer, A. Wierzbicka, P. Young, B. Högl, CL. Bassetti, M. Manconi, E. Feketeova, J. Mathis, T. Paiva, F. Canellas, M. Lecendreux, CR. Baumann, L. Barateau, C. Pesenti, E. Antelmi, C. Gaig, A. Iranzo, L. Lillo-Triguero, P. Medrano-Martínez, J. Haba-Rubio, C. Gorban, G. Luca, GJ. Lammers, R. Khatami,
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- $a 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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- $a Plazzi, Giuseppe $u Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater Studiorum, University of Bologna, Bologna, Italy. IRCCS Istituto delle Scienze Neurologiche di Bologna, ASL di Bologna, Bologna, Italy.
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- 700 1_
- $a Fronczek, Rolf $u Department of Neurology and Clinical Neurophysiology, Leiden University Medical Center, Leiden, The Netherlands. Sleep Wake Center SEIN Heemstede, Stichting Epilepsie Instellingen Nederland, Heemstede, The Netherlands.
- 700 1_
- $a Santamaria, Joan $u Neurology Service, Multidisciplinary Sleep Unit, Hospital Clínic of Barcelona, IDIBAPS, CIBERNED, Barcelona, Spain.
- 700 1_
- $a Partinen, Markku $u Helsinki Sleep Clinic, Vitalmed Research Center, Helsinki, Finland.
- 700 1_
- $a Overeem, Sebastiaan $u Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands. Eindhoven University of Technology, Eindhoven, The Netherlands.
- 700 1_
- $a Peraita-Adrados, Rosa $u Sleep and Epilepsy Unit, Clinical Neurophysiology Department, Gregorio Marañón University Hospital, Complutense University of Madrid (UCM), Madrid, Spain.
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- $a da Silva, Antonio Martins $u Serviço de Neurofisiologia, Hospital Santo António/Centro Hospitalar do Porto and Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal.
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- 700 1_
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- 700 1_
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- 700 1_
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- 700 1_
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- 700 1_
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