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The review aims to assess the applications of machine learning in the diagnosis of central disorders of hypersomnolence.

June 10, 2026Open Access

Machine Learning for Diagnosis and Differentiation of Central Disorders of Hypersomnolence: A Systematic Review.

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Systematic review explores machine learning's diagnostic potential for central disorders of hypersomnolence, indicating future implications.

The review aims to assess the applications of machine learning in the diagnosis of central disorders of hypersomnolence.
Conducted a systematic search across multiple databases following PRISMA guidelines.
Data types included neuroimaging, sleep assessments, and standardized questionnaires.
Extracted and analyzed machine learning methods and diagnostic outcomes from selected studies.
41 studies met inclusion criteria, with 37 peer-reviewed articles and 4 preprints.
Supervised learning identified features like early REM onset with strong accuracy for narcolepsy type 1.
Unsupervised learning revealed heterogeneous phenotypes, exposing limitations in current diagnostic classifications.

Helmy et al. (2026) studied this question.

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Systematic review explores machine learning's diagnostic potential for central disorders of hypersomnolence, indicating future implications.