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

Public (e)Health, Digital Epidemiology and Public Health Informatics (705) Decision Support for Health Professionals (1168) Machine Learning (1445) Intentional Self-Harm (96) Artificial Intelligence (1346) Clinical Information and Decision Making (1382) Depression and Mood Disorders; Suicide Prevention (1352)

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 (10) Tweetations (3) Metrics

Journals

  1. Woo H, Kim H, Park J, Lee J, Lee H, Kim M, Koyanagi A, Smith L, Rahmati M, Yeo S, Yon D. Global burden of vaccine‐associated multiple sclerosis, 1967–2022: A comprehensive analysis of the international pharmacovigilance database. Journal of Medical Virology 2024;96(4) View
  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 2024:1 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