Sex Differences in Etiologies of Sleep Disorders

Choi, Soo Joo; Kim, Dongyeop; Hwang, Yoonha; Jo, Hyunjin; Joo, Eun Yeon

doi:10.13078/jsm.200023

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…Insomnia, a clinical sleep disorder characterized by difficulty falling asleep, staying asleep, or waking up too early, leads to daytime impairment, including fatigue, attention problems, mood changes, or impaired performance; it is estimated that over one-third of individuals with insomnia experience chronic symptoms [ 7 , 8 ]. Diagnosis of insomnia is typically based on clinical symptoms rather than sleep parameters measured by overnight polysomnography (PSG), although PSG can identify potential underlying causes of insomnia-related symptoms [ 9 , 10 ]. OSA is characterized by repeated episodes of partial or complete upper airway obstruction during sleep, which can lead to sleep fragmentation and poor sleep quality.…”

Section: Introductionmentioning

confidence: 99%

“…COMISA is associated with increased medical and psychiatric morbidity and worse daytime functioning relative to each condition alone [15][16][17][18][19][20]. However, most people with COMISA do not recognize the possibility of accompanying OSA and do not undergo OSA evaluation [10,14,15]. Thus, it is clinically critical to differentiate patients with COMISA from insomnia patients without specific sleep disorders to cause insomnia symptoms, which requires the PSG test.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the Risk of Sleep Disorders Using a Machine Learning–Based Simple Questionnaire: Development and Validation Study

Ha,

Choi,

Lee

et al. 2023

J Med Internet Res

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Background Sleep disorders, such as obstructive sleep apnea (OSA), comorbid insomnia and sleep apnea (COMISA), and insomnia are common and can have serious health consequences. However, accurately diagnosing these conditions can be challenging as a result of the underrecognition of these diseases, the time-intensive nature of sleep monitoring necessary for a proper diagnosis, and patients’ hesitancy to undergo demanding and costly overnight polysomnography tests. Objective We aim to develop a machine learning algorithm that can accurately predict the risk of OSA, COMISA, and insomnia with a simple set of questions, without the need for a polysomnography test. Methods We applied extreme gradient boosting to the data from 2 medical centers (n=4257 from Samsung Medical Center and n=365 from Ewha Womans University Medical Center Seoul Hospital). Features were selected based on feature importance calculated by the Shapley additive explanations (SHAP) method. We applied extreme gradient boosting using selected features to develop a simple questionnaire predicting sleep disorders (SLEEPS). The accuracy of the algorithm was evaluated using the area under the receiver operating characteristics curve. Results In total, 9 features were selected to construct SLEEPS. SLEEPS showed high accuracy, with an area under the receiver operating characteristics curve of greater than 0.897 for all 3 sleep disorders, and consistent performance across both sets of data. We found that the distinction between COMISA and OSA was critical for accurate prediction. A publicly accessible website was created based on the algorithm that provides predictions for the risk of the 3 sleep disorders and shows how the risk changes with changes in weight or age. Conclusions SLEEPS has the potential to improve the diagnosis and treatment of sleep disorders by providing more accessibility and convenience. The creation of a publicly accessible website based on the algorithm provides a user-friendly tool for assessing the risk of OSA, COMISA, and insomnia.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting the Risk of Sleep Disorders Using a Machine Learning–Based Simple Questionnaire: Development and Validation Study

Ha,

Choi,

Lee

et al. 2023

J Med Internet Res

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Sex/Gender Differences in Sleep Physiology and Sleep Disorders

Lee

2024

Sex/Gender-Specific Medicine in Clinical Areas

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Predicting the Risk of Sleep Disorders Using a Machine Learning–Based Simple Questionnaire: Development and Validation Study (Preprint)

Ha,

Choi,

Lee

et al. 2023

Preprint

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BACKGROUND Sleep disorders, such as obstructive sleep apnea (OSA), comorbid insomnia and sleep apnea (COMISA), and insomnia are common and can have serious health consequences. However, accurately diagnosing these conditions can be challenging as a result of the underrecognition of these diseases, the time-intensive nature of sleep monitoring necessary for a proper diagnosis, and patients’ hesitancy to undergo demanding and costly overnight polysomnography tests. OBJECTIVE We aim to develop a machine learning algorithm that can accurately predict the risk of OSA, COMISA, and insomnia with a simple set of questions, without the need for a polysomnography test. METHODS We applied extreme gradient boosting to the data from 2 medical centers (n=4257 from Samsung Medical Center and n=365 from Ewha Womans University Medical Center Seoul Hospital). Features were selected based on feature importance calculated by the Shapley additive explanations (SHAP) method. We applied extreme gradient boosting using selected features to develop a simple questionnaire predicting sleep disorders (SLEEPS). The accuracy of the algorithm was evaluated using the area under the receiver operating characteristics curve. RESULTS In total, 9 features were selected to construct SLEEPS. SLEEPS showed high accuracy, with an area under the receiver operating characteristics curve of greater than 0.897 for all 3 sleep disorders, and consistent performance across both sets of data. We found that the distinction between COMISA and OSA was critical for accurate prediction. A publicly accessible website was created based on the algorithm that provides predictions for the risk of the 3 sleep disorders and shows how the risk changes with changes in weight or age. CONCLUSIONS SLEEPS has the potential to improve the diagnosis and treatment of sleep disorders by providing more accessibility and convenience. The creation of a publicly accessible website based on the algorithm provides a user-friendly tool for assessing the risk of OSA, COMISA, and insomnia.

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Sex Differences in Etiologies of Sleep Disorders

Cited by 3 publications

References 33 publications

Predicting the Risk of Sleep Disorders Using a Machine Learning–Based Simple Questionnaire: Development and Validation Study

Predicting the Risk of Sleep Disorders Using a Machine Learning–Based Simple Questionnaire: Development and Validation Study

Sex/Gender Differences in Sleep Physiology and Sleep Disorders

Predicting the Risk of Sleep Disorders Using a Machine Learning–Based Simple Questionnaire: Development and Validation Study (Preprint)

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