Repository logo
 
Publication

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

2018-07-13Journal article Open access
Simple item page
dc.contributor.authorZhang, Z.
dc.contributor.authorMayer, G.
dc.contributor.authorDauvilliers, Y.
dc.contributor.authorPlazzi, G.
dc.contributor.authorPizza, F.
dc.contributor.authorFronczek, R.
dc.contributor.authorSantamaria, J.
dc.contributor.authorPartinen, M.
dc.contributor.authorOvereem, S.
dc.contributor.authorPeraita-Adrados, R.
dc.contributor.authorSilva, A.
dc.contributor.authorSonka, K.
dc.contributor.authorRio-Villegas, R.
dc.contributor.authorHeinzer, R.
dc.contributor.authorWierzbicka, A.
dc.contributor.authorYoung, P.
dc.contributor.authorHögl, B.
dc.contributor.authorBassetti, C.
dc.contributor.authorManconi, M.
dc.contributor.authorFeketeova, E.
dc.contributor.authorMathis, J.
dc.contributor.authorPaiva, T.
dc.contributor.authorCanellas, F.
dc.contributor.authorLecendreux, M.
dc.contributor.authorBaumann, C.
dc.contributor.authorBarateau, L.
dc.contributor.authorPesenti, C.
dc.contributor.authorAntelmi, E.
dc.contributor.authorGaig, C.
dc.contributor.authorIranzo, A.
dc.contributor.authorLillo-Triguero, L.
dc.contributor.authorMedrano-Martínez, P.
dc.contributor.authorHaba-Rubio, J.
dc.contributor.authorGorban, C.
dc.contributor.authorLuca, G.
dc.contributor.authorLammers, G.
dc.contributor.authorKhatami, R.
dc.date.accessioned2020-03-21T17:40:47Z
dc.date.available2020-03-21T17:40:47Z
dc.date.issued2018-07-13
dc.description.abstractNarcolepsy 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.pt_PT
dc.description.sponsorshipThe 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationZhang 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.pt_PT
dc.identifier.doi10.1038/s41598-018-28840-wpt_PT
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/10400.16/2339
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherNature Researchpt_PT
dc.relation.publisherversionhttps://www.nature.com/articles/s41598-018-28840-wpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectHumanspt_PT
dc.subjectNarcolepsypt_PT
dc.subjectPolysomnographypt_PT
dc.subjectROC Curvept_PT
dc.subjectRare Diseasespt_PT
dc.subjectSleep Latencypt_PT
dc.subjectSleep, REMpt_PT
dc.subjectStochastic Processespt_PT
dc.subjectModels, Biologicalpt_PT
dc.subjectSupervised Machine Learningpt_PT
dc.titleExploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learningpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceEnglandpt_PT
oaire.citation.issue1pt_PT
oaire.citation.startPage10628pt_PT
oaire.citation.titleScientific reportspt_PT
oaire.citation.volume8pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Exploring the Clinical Features of Narcolepsy Type 1.pdf
Size:
1.39 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.35 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

SNF - Artigos publicados em revistas indexadas na Pubmed/Medline