ArticleJournal of medical Internet research2023
Predicting the Risk of Sleep Disorders Using a Machine Learning-Based Simple Questionnaire: Development and Validation Study.
Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025Pooled it
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- A machine learning-based screening tool for classifying post-traumatic stress disorder in firefighters: Firefighter Latent Risk Evaluation (FLARE).BMC psychiatry · 2026Article
- Obstructive sleep apnea: Proposed scoring system.World journal of methodology · 2026Review
- Frailty-Driven Prediction of Inpatient Obstructive Sleep Apnea and Related Sleep Disorder Diagnoses Using Explainable AI.Biomedicines · 2026Article
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- Preventive circadian medicine: improving health with sleep checkups.Npj biological timing and sleep · 2025Review
- Risk warning model for predicting sleep disorders in healthcare workers on long-term shifts.Sleep and biological rhythms · 2025Article
- A novel machine learning model for screening the risk of obstructive sleep apnea using craniofacial photography with questionnaires.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- Abbreviated Duke Activity Status Index for Risk Stratification in Heart Failure.The American journal of cardiology · 2025Article
- Real-time sleep disorder monitoring design using dynamic temporal graphs with facial and acoustic feature fusion.Frontiers in artificial intelligence · 2025Article
- Exploring predictors of insomnia severity in shift workers using machine learning model.Frontiers in public health · 2025Article
- Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features.NPJ digital medicine · 2024Article
- A Risk Correlative Model for Sleep Disorders in Chinese Older Adults Based on Blood Micronutrient Levels: A Matched Case-Control Study.Nutrients · 2024Article
- A multimodal multi-agent LLM framework for identifying key drivers of sleep disorders.Frontiers in neurologyArticle
- Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSleep 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.
objectiveWe 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.
methodsWe 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.
resultsIn 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.
conclusionsSLEEPS 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.