2026-04-29T05:37:38Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/225952024-11-28T17:45:43Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16961col_20.500.12105_16967

00925njm 22002777a 4500 dc Zhang, Zhongxing author Mayer, Geert author Dauvilliers, Yves author Plazzi, Giuseppe author Pizza, Fabio author Fronczek, Rolf author Santamaria Semis, Joan author Partinen, Markku author Overeem, Sebastiaan author Peraita-Adrados, Rosa author da Silva, Antonio Martins author Sonka, Karel author del Rio-Villegas, Rafael author Heinzer, Raphael author Wierzbicka, Aleksandra author Young, Peter author Hoegl, Birgit author Bassetti, Claudio L author Manconi, Mauro author Feketeova, Eva author Mathis, Johannes author Paiva, Teresa author Cañellas, Francesca author Lecendreux, Michel author Baumann, Christian R author Barateau, Lucie author Pesenti, Carole author Antelmi, Elena author Gaig, Carles author Iranzo, Alex author Lillo-Triguero, Laura author Medrano-Martinez, Pablo author Haba-Rubio, Jose author Gorban, Corina author Luca, Gianina author Lammers, Gert Jan author Khatami, Ramin author 2018-07-13 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 rapideye-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. Zhang Z, Mayer G, Dauvilliers Y, Plazzi G, Pizza F, Fronczek R, 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 Jul 13;8:10628. 2045-2322 http://hdl.handle.net/20.500.13003/9214 https://hdl.handle.net/20.500.12105/22595 30006563 10.1038/s41598-018-28840-w Scientific Reports 2-s2.0-85049967571 438489600004 L629416793 Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning