ArticleEuropean journal of neurology2024
Disentangling the complex landscape of sleep-wake disorders with data-driven phenotyping: A study of the Bernese center.
Article in European journal of neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Machine Learning for Diagnosis and Differentiation of Central Disorders of Hypersomnolence: A Systematic Review.European journal of neurology · 2026Pooled it
- iSPHYNCS: Unsupervised Clustering in Questionnaires and Metadata Reveals Distinct Subtypes in the Narcolepsy Borderland.Journal of sleep research · 2026Article
- Disentangling the complex landscape of sleep-wake disorders with data-driven phenotyping: A study of the Bernese center.European journal of neurology · 2024Article
- Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring Algorithm with Uncertainty-Guided Physician Review.Nature and science of sleep · 2024Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
background and purposeThe diagnosis of sleep-wake disorders (SWDs) is challenging because of the existence of only few accurate biomarkers and the frequent coexistence of multiple SWDs and/or other comorbidities. The aim of this study was to assess in a large cohort of well-characterized SWD patients the potential of a data-driven approach for the identification of SWDs.
methodsWe included 6958 patients from the Bernese Sleep Registry and 300 variables/biomarkers including questionnaires, results of polysomnography/vigilance tests, and final clinical diagnoses. A pipeline, based on machine learning, was created to extract and cluster the clinical data. Our analysis was performed on three cohorts: patients with central disorders of hypersomnolence (CDHs), a full cohort of patients with SWDs, and a clean cohort without coexisting SWDs.
resultsA first analysis focused on the cohort of patients with CDHs and revealed four patient clusters: two clusters for narcolepsy type 1 (NT1) but not for narcolepsy type 2 or idiopathic hypersomnia. In the full cohort of SWDs, nine clusters were found: four contained patients with obstructive and central sleep apnea syndrome, one with NT1, and four with intermixed SWDs. In the cohort of patients without coexisting SWDs, an additional cluster of patients with chronic insomnia disorder was identified.
conclusionsThis study confirms the existence of clear clusters of NT1 in CDHs, but mainly intermixed groups in the full spectrum of SWDs, with the exception of sleep apnea syndromes and NT1. New biomarkers are needed for better phenotyping and diagnosis of SWDs.
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