{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T00:59:15Z","timestamp":1775869155327,"version":"3.50.1"},"reference-count":419,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T00:00:00Z","timestamp":1753660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,10]]},"abstract":"Abstract<\/jats:title>\n \n Study Objectives<\/jats:title>\n Unsupervised machine learning\u2014an approach that identifies patterns and structures within data without relying on labels\u2014has demonstrated remarkable success in various domains of sleep research. This underscores the broader utility of machine learning, suggesting that its capabilities extend beyond current applications and warrant further exploration for novel insights in sleep studies, focusing specifically on unsupervised machine learning.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n This paper outlines a scoping review conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for scoping reviews. A comprehensive search covering various search terms focusing on the intersection between unsupervised machine learning and sleep led to 3960 publications. After screening all titles and abstracts with two independent reviewers, ultimately, 356 publications were included in the full-text review. The data extracted from the full texts included information about the machine learning methods and types of sleep data, as well as the study population.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n There has been a steep increase in the number of publications in this research area in the past 10\u00a0years. Clustering is the most commonly used method, but other methods are gaining popularity. Apart from classical polysomnography, data from wearable devices, nearables, video, audio, and medical imaging techniques have been used as input to unsupervised machine learning. The broad search allowed us to explore various applications within sleep research, ranging from the general population to populations with various sleep disorders.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n The review mapped existing research on unsupervised learning in sleep research, identified gaps in the literature, and derived directions for future research.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/sleep\/zsaf189","type":"journal-article","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T14:18:03Z","timestamp":1752502683000},"source":"Crossref","is-referenced-by-count":2,"title":["Unsupervised machine learning in sleep research: a scoping review"],"prefix":"10.1093","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0974-202X","authenticated-orcid":false,"given":"Luka","family":"Biedebach","sequence":"first","affiliation":[{"name":"Department of Computer Science, Reykjavik University , Reykjavik ,","place":["Iceland"]},{"name":"Reykjavik 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