This paper is in the following e-collection/theme issue:

Artificial Intelligence (2129) Sleep Monitoring, Sleep Quality, Sleep Disorders (268) Machine Learning (2076) Decision Support for Health Professionals (1539) Insomnia and Sleep Hygiene (248) Clinical Information and Decision Making (1915)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46520, first published .
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

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

Authors of this article:

Seokmin Ha1, 2 Author Orcid Image ;   Su Jung Choi3 Author Orcid Image ;   Sujin Lee4 Author Orcid Image ;   Reinatt Hansel Wijaya1, 5 Author Orcid Image ;   Jee Hyun Kim6 Author Orcid Image ;   Eun Yeon Joo4 Author Orcid Image ;   Jae Kyoung Kim1, 2 Author Orcid Image
Article Authors Cited by (26) Tweetations (7) Metrics

Journals

  1. Jo H, Jeon H, Ahn J, Jeon S, Kim J, Chung S. Dysfunctional Beliefs and Attitudes about Sleep-6 (DBAS-6): Data-driven shortened version from a machine learning approach. Sleep Medicine 2024;119:312 View
  2. Cheng C, Chen X, Zhang L, Wang Z, Duan H, Wu Q, Yan R, Wang D, Li Z, He R, Li Z, Chen Y, Ma F, Du Y, Li W, Huang G. A Risk Correlative Model for Sleep Disorders in Chinese Older Adults Based on Blood Micronutrient Levels: A Matched Case-Control Study. Nutrients 2024;16(19):3306 View
  3. Lim D, Jeong J, Song Y, Cho C, Yeom J, Lee T, Lee J, Lee H, Kim J. Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features. npj Digital Medicine 2024;7(1) View
  4. Augusto S, Wu Y, Chaikijurajai T, Hazen S, Tang W. Abbreviated Duke Activity Status Index for Risk Stratification in Heart Failure. The American Journal of Cardiology 2025;237:54 View
  5. Cawiding O, Lee S, Jo H, Kim S, Suh S, Joo E, Chung S, Kim J. SymScore: Machine learning accuracy meets transparency in a symbolic regression-based clinical score generator. Computers in Biology and Medicine 2025;185:109589 View
  6. Jeong E, Kim J. Less Is More: Machine Learning-Based Shortened Sleep Questionnaires for Efficient Clinical Practice. Sleep Medicine Research 2024;15(4):216 View
  7. Yeo H, Jang H, Kim N, Jeon S, Hwang Y, Kang C, Kim S. Exploring predictors of insomnia severity in shift workers using machine learning model. Frontiers in Public Health 2025;13 View
  8. Wan Q, Liu K, Bo Y, Yuan X, Li M, Wang X, Chen C, Liu L, Wu W. Predicting Insomnia Response to Acupuncture With the Development of Innovative Machine Learning. IEEE Access 2025;13:45964 View
  9. Li X, Xiao L, Shi B, Liu N, Dong L, Lyu R, Qian M. Risk warning model for predicting sleep disorders in healthcare workers on long-term shifts. Sleep and Biological Rhythms 2025;23(3):331 View
  10. Park J, Shin H, Kim M, Kim Y, Ryu W, Kim E, Chang H, Lee W, Kim J, Kim T. A novel machine learning model for screening the risk of obstructive sleep apnea using craniofacial photography with questionnaires. Journal of Clinical Sleep Medicine 2025;21(5):843 View
  11. Saadatfar H, Khademi S, Behdani B, Eshghi A. Insomnia in university students: a review of etiological factors and the advancement of data analytic techniques. Sleep and Breathing 2025;29(3) View
  12. Cawiding O, Jo H, Jeon S, Ardi F, Kim J, Chung S. Cancer-related Dysfunctional Beliefs and Attitude about Sleep-6 (C-DBAS-6): a practical and accurate shortened version using XGBoost and SymScore. Sleep and Biological Rhythms 2025;23(4):465 View
  13. Luo X, Li Y, Xu J, Zheng Z, Ying F, Huang G. AI in Medical Questionnaires: Scoping Review. Journal of Medical Internet Research 2025;27:e72398 View
  14. Mohammed-Taha S, Aladdin A, Mustafa A, Hasan D, Muhammed R, Rashid T. Analyzing the impact of the pandemic on insomnia prediction using machine learning classifiers across demographic groups. Network Modeling Analysis in Health Informatics and Bioinformatics 2025;14(1) View
  15. Zhang R, Luo J, Wang T, Wang W, Sun J, Zhang D. Integrating metabolomics, proteomics, and traditional risk factors to predict sleep disorders and elucidate potential biological pathways. Journal of Affective Disorders 2025;390:119784 View
  16. Wang X, Zhang D, Lu L, Meng S, Li Y, Zhang R, Zhou J, Yu Q, Zeng L, Zhao J, Zeng Y, Gao R. Development and validation of an explainable machine learning model for predicting the risk of sleep disorders in older adults with multimorbidity: a cross-sectional study. Frontiers in Public Health 2025;13 View
  17. Jeon S, Jeong E, Bang Y, Ahn J, Yoo S, Kim J, Chung S. Machine-Learning Validated Short Form of the Korean Version of the Sleep-Related Behaviors Questionnaire-10 Items: SRBQ-10. Behavioral Sleep Medicine 2025:1 View
  18. Minami Y, Kishi A, Ueda H. Preventive circadian medicine: improving health with sleep checkups. npj Biological Timing and Sleep 2025;2(1) View
  19. Casal-Guisande M, Mosteiro-Añón M, Torres-Durán M, Comesaña-Campos A, Fernández-Villar A. Application of artificial intelligence for the detection of obstructive sleep apnea based on clinical and demographic data: a systematic review. Expert Review of Respiratory Medicine 2025:1 View
  20. Cawiding O, Bae H, Kim J, Joo E, Kim J. Simplified and accurate approach to sleep disorder prediction using body composition metrics: a development and validation study. SLEEPJ 2025 View
  21. Pei F, Zhou Y, Fu Q, Zhou H. Real-time sleep disorder monitoring design using dynamic temporal graphs with facial and acoustic feature fusion. Frontiers in Artificial Intelligence 2025;8 View
  22. Gunduz Gurkan C. Rethinking prediction of obstructive sleep apnea and insomnia: integrating body composition for precision screening. SLEEPJ 2025 View

Conference Proceedings

  1. Ishaq S, Rehman A, Ali T, Javaid S, Mir A. 2024 International Conference on Engineering & Computing Technologies (ICECT). Exploring Sleep Paralysis Phenomenon Through Machine Learning: An Analytical Study View
  2. Nayyem M, Raju M, Al Rakin A, Sharif K, Arafin R, Sultana S. 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). Augmenting Sleep Quality Prognostics through Internet of Things and Machine Learning: A Rigorous Comparative Analysis for Advanced Personalized Health Metrics View
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  4. Wei Y, Xu B, Cai B, Wen H. Proceedings of the 2025 International Conference on Health Informatization and Data Analytics. Interpretable Machine Learning for Predicting Sleep Disorders among University Students: A SHAP-XGBoost Framework Based on Psychological Traits View