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

Artificial Intelligence (1734) Sleep Monitoring, Sleep Quality, Sleep Disorders (227) Machine Learning (1735) Decision Support for Health Professionals (1338) Insomnia and Sleep Hygiene (208) Clinical Information and Decision Making (1654)

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 (12) 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 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

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