Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning.

Détails

ID Serval
serval:BIB_F746179696E4
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning.
Périodique
Scientific reports
Auteur(s)
Zhang Z., Mayer G., Dauvilliers Y., Plazzi G., Pizza F., Fronczek R., Santamaria J., Partinen M., Overeem S., Peraita-Adrados R., da Silva A.M., Sonka K., Rio-Villegas R.D., Heinzer R., Wierzbicka A., Young P., Högl B., Bassetti C.L., Manconi M., Feketeova E., Mathis J., Paiva T., Canellas F., Lecendreux M., Baumann C.R., 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.J., Khatami R.
ISSN
2045-2322 (Electronic)
ISSN-L
2045-2322
Statut éditorial
Publié
Date de publication
13/07/2018
Peer-reviewed
Oui
Volume
8
Numéro
1
Pages
10628
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Résumé
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.
Pubmed
Web of science
Open Access
Oui
Création de la notice
03/08/2018 17:19
Dernière modification de la notice
20/08/2019 17:23
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