Duval, S., & Wicklund, R. A. (1972). A theory of objective self awareness.: Academic Press.
Yoon, H., & Kim, H. K. (2020). The Effects of University Students Self-Focused Attention on Depression: Mediated Moderation Effect of Rumination and Self-Compassion. Korean Journal of Youth Studies 27, 299–326,.
Kim, H., & Han, S. (2018). Does personal distress enhance empathic interaction or block it? Personality and Individual Differences, 124, 77–83.
Yoon, D., & Lee, Y. (2023). The Effect of Perceived Stress on Drinking Problem of Korean College Students: From the Perspective of Escape Theory. Korean Journal of Stress Research, 31, 1–10,.
Yoon, B., Hong, J. H., & Chung, S. C. (2024). Viral Anxiety, Reassurance-Seeking Behavior, and Depression Mediate the Influence of Dysfunctional Self-Focus on Preoccupation With COVID-19 Among Infected Cases. Psychiatry Investigation, 21(8), 897–904, doi:https://doi.org/10.30773/pi.2024.0097.
Article PubMed PubMed Central Google Scholar
Jrad, A. I. S., Yi, Y., Yoon, B., Cho, E., Cho, I. K., Lee, D., et al. (2024). Dysfunctional Self-Focus and Fear of Progression in Cancer Patients Mediated by Depression, Anxiety, and Dysfunctional Sleep Beliefs. Psychiatry Investigation, 21(5), 506–512, doi:https://doi.org/10.30773/pi.2023.0354.
Article PubMed PubMed Central Google Scholar
Wells, S. Y., Dietch, J. R., Edner, B. J., Glassman, L. H., Thorp, S. R., Morland, L. A., et al. (2021). The Development of a Brief Version of the Insomnia Severity Index (ISI-3) in Older Adult Veterans with Posttraumatic Stress Disorder. Behavioral Sleep Medicine, 19(3), 352–362, doi:https://doi.org/10.1080/15402002.2020.1760278.
Bang, Y. R., Cho, E., Ahmed, O., Lee, J., Pearson, L., Ahn, J., et al. (2022). Validation of the Korean Version of the Positive and Negative Sleep Appraisal Measure (PANSAM) as a Tool for Evaluating Dysfunctional Beliefs about Sleep among the General Population. Journal of Clinical Medicine, 11(16), doi:ARTN 467210.3390/jcm11164672
Chung, S. C., Ahmed, O., Cho, E. L., Bang, Y. R., Ahn, J., Choi, H., et al. (2024). Psychometric Properties of the Insomnia Severity Index and Its Comparison With the Shortened Versions Among the General Population. Psychiatry Investigation, 21(1), 9–17, doi:https://doi.org/10.30773/pi.2023.0189.
Article PubMed PubMed Central Google Scholar
Jo, H., Jeon, H. J., Ahn, J., Jeon, S., Kim, J. K., & Chung, S. (2024). Dysfunctional Beliefs and Attitudes about Sleep-6 (DBAS-6): Data-driven shortened version from a machine learning approach. Sleep Medicine, 119, 312–318, doi:https://doi.org/10.1016/j.sleep.2024.04.027.
Jo, H., Lim, M., Jeon, H. J., Ahn, J., Jeon, S., Kim, J. K., et al. (2024). Data-driven shortened Insomnia Severity Index (ISI): a machine learning approach. Sleep and Breathing, 28(4), 1819–1830, doi:https://doi.org/10.1007/s11325-024-03037-w.
Chung, S., Bang, Y. R., Shahrier, M. A., Hong, Y., & Ahn, J. (2025). Dysfunctional Pandemic Grief Model Among Nursing Professionals Who Experienced Death of Patients. Psychiatr Q, doi:https://doi.org/10.1007/s11126-025-10142-w.
Kim, H., & Lee, H. (2012). Development and validation of dysfunctional self-focus attributes scale. Kor J Clin Psychol, 31, 487–505.
Lowe, B., Kroenke, K., & Grafe, K. (2005). Detecting and monitoring depression with a two-item questionnaire (PHQ-2). J Psychosom Res, 58(2), 163–171, doi:https://doi.org/10.1016/j.jpsychores.2004.09.006.
Fabrigar, L. R., & Wegener, D. T. (2012). Exploratory factor analysis (Understanding statistics). Oxford; New York: Oxford University Press.
Byrne, B. M. (2012). Structural equation modeling with Mplus: basic concepts, applications, and programming (Multivariate applications series). New York: Routledge Academic.
Brown, T. A. (2015). Confirmatory factor analysis for applied research (Second edition. ed., Methodology in the social sciences). New York; London: The Guilford Press.
Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Paper presented at the Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA,
Zhang, X., Yan, C., Gao, C., Malin, B. A., & Chen, Y. (2020). Predicting Missing Values in Medical Data via XGBoost Regression. J Healthc Inform Res, 4(4), 383–394, doi:https://doi.org/10.1007/s41666-020-00077-1.
Article PubMed PubMed Central Google Scholar
Jiang, J., Pan, H., Li, M., Qian, B., Lin, X., & Fan, S. (2021). Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm. Sci Rep, 11(1), 5542, doi:https://doi.org/10.1038/s41598-021-85223-4.
Article CAS PubMed PubMed Central Google Scholar
Lee, J., Ha, S., Ahmed, O., Cho, I. K., Lee, D., Kim, K., et al. (2022). Validation of the Korean version of the Metacognitions Questionnaire-Insomnia (MCQ-I) scale and development of shortened versions using the random forest approach. Sleep Medicine, 98, 53–61, doi:https://doi.org/10.1016/j.sleep.2022.06.005.
Ha, S., Choi, S. J., Lee, S., Wijaya, R. H., Kim, J. H., Joo, E. Y., et al. (2023). Predicting the Risk of Sleep Disorders Using a Machine Learning-Based Simple Questionnaire: Development and Validation Study. Journal of Medical Internet Research, 25, doi:ARTN e4652010.2196/46520
Li, B., Zhang, F., Niu, Q., Liu, J., Yu, Y., Wang, P., et al. (2023). A molecular classification of gastric cancer associated with distinct clinical outcomes and validated by an XGBoost-based prediction model. Mol Ther Nucleic Acids, 31, 224–240, doi:https://doi.org/10.1016/j.omtn.2022.12.014.
Article CAS PubMed Google Scholar
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., et al. (2020). From Local Explanations to Global Understanding with Explainable AI for Trees. Nat Mach Intell, 2(1), 56–67, doi:https://doi.org/10.1038/s42256-019-0138-9.
Article PubMed PubMed Central Google Scholar
Cawiding, O. R., Lee, S., Jo, H., Kim, S., Suh, S., Joo, E. Y., et al. (2025). SymScore: Machine learning accuracy meets transparency in a symbolic regression-based clinical score generator. Comput Biol Med, 185, 109589, doi:https://doi.org/10.1016/j.compbiomed.2024.109589.
Cawiding, O. R., Jo, H., Jeon, S., Ardi, F. R., Kim, J. K., & Chung, S. C. (2025). 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, doi:https://doi.org/10.1007/s41105-025-00594-9.
Article PubMed PubMed Central Google Scholar
Kwon, C. Y., Lee, B., Kim, S. H., Jeong, S. C., & Kim, J. W. (2024). Development of a Short-Form Hwa-Byung Symptom Scale Using Machine Learning Approaches. Diagnostics (Basel), 14(21), doi:https://doi.org/10.3390/diagnostics14212419.
Kosmicki, J. A., Sochat, V., Duda, M., & Wall, D. P. (2015). Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning. Transl Psychiatry, 5(2), e514, doi:https://doi.org/10.1038/tp.2015.7.
Article CAS PubMed PubMed Central Google Scholar
Jeon, S., Jeong, E. M., Bang, Y. R., Ahn, J., Yoo, S., Kim, J. K., et al. (2025). Machine-Learning Validated Short Form of the Korean Version of the Sleep-Related Behaviors Questionnaire-10 Items: SRBQ-10. Behavioral Sleep Medicine, 1–14, doi:https://doi.org/10.1080/15402002.2025.2544975.
Comments (0)