{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T18:41:19Z","timestamp":1780598479646,"version":"3.54.1"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,1,6]],"date-time":"2024-01-06T00:00:00Z","timestamp":1704499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,2,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Study Objectives<\/jats:title><jats:p>Narcolepsy is a central hypersomnia disorder, and differential diagnoses between its subtypes can be difficult. Hence, we applied machine learning to analyze the positron emission tomography (PET) data of patients with type 1 or type 2 narcolepsy, and patients with type 1 narcolepsy and comorbid schizophrenia, to construct predictive models to facilitate the diagnosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This is a retrospective and prospective case\u2013control study of adolescent and young adult patients with type 1 or type 2 narcolepsy, and type 1 narcolepsy and comorbid schizophrenia. All participants received 18-F-fluorodeoxy glucose PET, sleep studies, neurocognitive tests, sleep questionnaires, and human leukocyte antigen typing. The collected PET data were analyzed by feature selections and classification methods in machine learning to construct predictive models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 314 participants with narcolepsy were enrolled; 204 had type 1 narcolepsy, 90 had type 2 narcolepsy, and 20 had type 1 narcolepsy and comorbid schizophrenia. We used three filter methods for feature selection followed by a comparative analysis of classification methods. To apply a small number of regions of interest (ROI) and high classification accuracy, the Na\u00efve Bayes classifier with the Term Variance as feature selection achieved the goal with only three ROIs (left basal ganglia, left Heschl, and left striatum) and produced an accuracy of higher than 99%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The accuracy of our predictive model of PET data are promising and can aid clinicians in the diagnosis of narcolepsy subtypes. Future research with a larger sample size could further refine the predictive model of narcolepsy.<\/jats:p><\/jats:sec>","DOI":"10.1093\/sleep\/zsad328","type":"journal-article","created":{"date-parts":[[2024,1,6]],"date-time":"2024-01-06T12:03:05Z","timestamp":1704542585000},"source":"Crossref","is-referenced-by-count":7,"title":["The application of machine learning on brain imaging features of different narcolepsy subtypes"],"prefix":"10.1093","volume":"47","author":[{"given":"Wei-Chih","family":"Chin","sequence":"first","affiliation":[{"name":"Department of Child Psychiatry and Sleep Center, Chang Gung Memorial Hospital and Chang Gung University College of Medicine , Taoyuan , Taiwan"},{"name":"College of Life Sciences and Medicine, National Tsing Hua University , Hsinchu , 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