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

Artificial Intelligence (1409) Sleep Monitoring, Sleep Quality, Sleep Disorders (188) Machine Learning (1491) Decision Support for Health Professionals (1203) Insomnia and Sleep Hygiene (175) Clinical Information and Decision Making (1426)

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 (5) Tweetations (8) Metrics

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  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