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

Public (e)Health, Digital Epidemiology and Public Health Informatics (743) Decision Support for Health Professionals (1201) Machine Learning (1489) Intentional Self-Harm (100) Artificial Intelligence (1408) Clinical Information and Decision Making (1424) Depression and Mood Disorders; Suicide Prevention (1386)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/51473, first published .
Machine Learning–Based Prediction of Suicidality in Adolescents With Allergic Rhinitis: Derivation and Validation in 2 Independent Nationwide Cohorts

Machine Learning–Based Prediction of Suicidality in Adolescents With Allergic Rhinitis: Derivation and Validation in 2 Independent Nationwide Cohorts

Machine Learning–Based Prediction of Suicidality in Adolescents With Allergic Rhinitis: Derivation and Validation in 2 Independent Nationwide Cohorts

Authors of this article:

Hojae Lee1, 2 Author Orcid Image ;   Joong Ki Cho3 Author Orcid Image ;   Jaeyu Park1, 2 Author Orcid Image ;   Hyeri Lee1, 2 Author Orcid Image ;   Guillaume Fond4 Author Orcid Image ;   Laurent Boyer4 Author Orcid Image ;   Hyeon Jin Kim1, 2 Author Orcid Image ;   Seoyoung Park5 Author Orcid Image ;   Wonyoung Cho1 Author Orcid Image ;   Hayeon Lee2, 5 Author Orcid Image ;   Jinseok Lee5, 6 Author Orcid Image ;   Dong Keon Yon1, 2, 7 Author Orcid Image
Article Authors Cited by (14) Tweetations (3) Metrics

Journals

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  2. Kim H, Son Y, Lee H, Kang J, Hammoodi A, Choi Y, Kim H, Lee H, Fond G, Boyer L, Kwon R, Woo S, Yon D. Machine Learning–Based Prediction of Suicidal Thinking in Adolescents by Derivation and Validation in 3 Independent Worldwide Cohorts: Algorithm Development and Validation Study. Journal of Medical Internet Research 2024;26:e55913 View
  3. Kim Y, Lee H, Woo H, Lee S, Hong M, Jung E, Yoo S, Lee J, Yon D, Kang B. Machine learning-based model to predict delirium in patients with advanced cancer treated with palliative care: a multicenter, patient-based registry cohort. Scientific Reports 2024;14(1) View
  4. Kattih M, Lee H, Jo H, Jeong J, Kim H, Park J, Yang H, Nguyen A, Kim H, Lee H, Kim M, Lee M, Kwon R, Kim S, Koyanagi A, Kim M, Rahmati M, López Sánchez G, Dragioti E, Kim J, Woo S, Cho S, Smith L, Yon D. National prevalence of atopic dermatitis in Korean adolescents from 2009 to 2022. Scientific Reports 2024;14(1) View
  5. Guo S, Qing G, Yang G. The relationship between chronic disease variety and quantity and suicidal ideation: A cross-sectional study of NHANES. Journal of Psychosomatic Research 2024;184:111854 View
  6. Jeong J, Jo H, Son Y, Lee S, Lee K, Choi Y, Lee H, Kim S, Jacob L, Smith L, Lee J, Rhee S, Kim S, Kang J, Hwang J, Park J, Woo S, Yon D. Association of Soda Drinks and Fast Food with Allergic Diseases in Korean Adolescents: A Nationwide Representative Study. International Archives of Allergy and Immunology 2024;185(12):1190 View
  7. Jiang S, Yang S, Deng K, Jiang R, Xue Y. Machine learning models for diagnosing Alzheimer’s disease using brain cortical complexity. Frontiers in Aging Neuroscience 2024;16 View
  8. Zhang J, Feng X, Wang W, Liu S, Zhang Q, Wu D, Liu Q. Predicting the Risk of Loneliness in Children and Adolescents: A Machine Learning Study. Behavioral Sciences 2024;14(10):947 View
  9. Park S, Son Y, Lee H, Lee H, Lee J, Kang J, Smith L, Rahmati M, Dragioti E, Tully M, Fond G, Boyer L, Lee J, Pizzol D, Park J, Woo S, Yon D. Sex-Specific Trends in the Prevalence of Osteoarthritis and Rheumatoid Arthritis From 2005 to 2021 in South Korea: Nationwide Cross-Sectional Study. JMIR Public Health and Surveillance 2024;10:e57359 View
  10. Lee J, Son Y, Park J, Lee H, Choi Y, Lee M, Kim S, Kang J, Oh J, Kim H, Rhee S, Smith L, Yon D. Comparison of national trends in physical activity among adolescents before and during the COVID-19 pandemic: A nationally representative serial study in South Korea. Heliyon 2024;10(21):e40004 View
  11. Hwang S, Lee H, Lee J, Lee M, Koyanagi A, Smith L, Rhee S, Yon D, Lee J. Machine Learning–Based Prediction for Incident Hypertension Based on Regular Health Checkup Data: Derivation and Validation in 2 Independent Nationwide Cohorts in South Korea and Japan. Journal of Medical Internet Research 2024;26:e52794 View
  12. Sang H, Park J, Kim S, Lee M, Lee H, Lee S, Yon D, Rhee S. Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea. Scientific Reports 2024;14(1) View
  13. Jo H, Park J, Lee H, Lee K, Lee H, Son Y, Kang J, Lee S, Choi Y, Lee J, Fond G, Boyer L, Smith L, Lee J, López Sánchez G, Dragioti E, Tully M, Rahmati M, Woo H, Woo S, Yon D. Nationwide trends in sadness, suicidal ideation, and suicide attempts among multicultural and monocultural adolescents in South Korea during the COVID-19 pandemic, 2011–2022. World Journal of Pediatrics 2024 View
  14. Jang W, Kim S, Son Y, Kim S, Lee H, Park J, Lee K, Kang J, Pizzol D, Hwang J, Rhee S, Yon D. Non-Linear Association Between Physical Activities and Type 2 Diabetes in 2.4 Million Korean Population, 2009–2022: A Nationwide Representative Study. Journal of Korean Medical Science 2025;40 View