ArticleScientific reports2018
Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- Machine Learning for Diagnosis and Differentiation of Central Disorders of Hypersomnolence: A Systematic Review.European journal of neurology · 2026Pooled it
- Utilisation of machine learning to predict surgical candidates for the treatment of childhood upper airway obstruction.Sleep & breathing = Schlaf & Atmung · 2022Trial
- iSPHYNCS: Unsupervised Clustering in Questionnaires and Metadata Reveals Distinct Subtypes in the Narcolepsy Borderland.Journal of sleep research · 2026Article
- Article
- Present and Future of Central Disorders of Hypersomnolence.Journal of sleep research · 2025Review
- Quantification of electromyographic activity during REM sleep in Chinese narcolepsy patients and healthy controls: a comparative study.Sleep & breathing = Schlaf & Atmung · 2025Article
- Impact of Age at Narcolepsy Onset on Sleep-Onset REM Periods in the Multiple Sleep Latency Test.Journal of clinical medicine · 2025Article
- Narcolepsy and rapid eye movement sleep.Journal of sleep research · 2025Review
- Identifying time-resolved features of nocturnal sleep characteristics of narcolepsy using machine learning.Journal of sleep research · 2024Article
- Causal Association Between Subtypes of Excessive Daytime Sleepiness and Risk of Cardiovascular Diseases.Journal of the American Heart Association · 2023Article
- A series of 7 cases of patients with narcolepsy with hypocretin deficiency without the HLA DQB1*06:02 allele.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2023Article
- Serum metabolomics study of narcolepsy type 1 based on ultra-performance liquid chromatography-tandem mass spectrometry.Amino acids · 2023Article
- Comparative Efficacy and Safety of Multiple Wake-Promoting Agents for the Treatment of Excessive Daytime Sleepiness in Narcolepsy: A Network Meta-Analysis.Nature and science of sleep · 2023Review
- The impact of narcolepsy on social relationships in young adults.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2022Article
- Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering.Neurology · 2022Observational
- Idling for Decades: A European Study on Risk Factors Associated with the Delay Before a Narcolepsy Diagnosis.Nature and science of sleep · 2022Article
- The Swiss Primary Hypersomnolence and Narcolepsy Cohort study (SPHYNCS): Study protocol for a prospective, multicentre cohort observational study.Journal of sleep research · 2021Article
- Animal models of narcolepsy and the hypocretin/orexin system: Past, present, and future.Sleep · 2021Article
- Recognizing the Symptom Spectrum of Narcolepsy to Improve Timely Diagnosis: A Narrative Review.Nature and science of sleep · 2021Review
- Case report: narcolepsy type 2 due to temporal lobe glioma.Medicine · 2020Article
Corrections and comments
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Authors and funding
37 authors at 19 institutions in 11 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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