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

Artificial Intelligence (1720) Depression and Mood Disorders; Suicide Prevention (1514) Machine Learning (1730) Mental Health Surveillance and Epidemiology (77) Intentional Self-Harm (112) Mental Health Issues in Adolescence (523) Methods and New Tools in Mental Health Research (497)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55913, first published .
Machine Learning–Based Prediction of Suicidal Thinking in Adolescents by Derivation and Validation in 3 Independent Worldwide Cohorts: Algorithm Development and Validation Study

Machine Learning–Based Prediction of Suicidal Thinking in Adolescents by Derivation and Validation in 3 Independent Worldwide Cohorts: Algorithm Development and Validation Study

Machine Learning–Based Prediction of Suicidal Thinking in Adolescents by Derivation and Validation in 3 Independent Worldwide Cohorts: Algorithm Development and Validation Study

Authors of this article:

Hyejun Kim1, 2 Author Orcid Image ;   Yejun Son1, 3 Author Orcid Image ;   Hojae Lee1, 4 Author Orcid Image ;   Jiseung Kang5, 6 Author Orcid Image ;   Ahmed Hammoodi7 Author Orcid Image ;   Yujin Choi1, 8 Author Orcid Image ;   Hyeon Jin Kim1, 4 Author Orcid Image ;   Hayeon Lee1 Author Orcid Image ;   Guillaume Fond9 Author Orcid Image ;   Laurent Boyer9 Author Orcid Image ;   Rosie Kwon1 Author Orcid Image ;   Selin Woo1 Author Orcid Image ;   Dong Keon Yon1, 3, 4, 10 Author Orcid Image
Article Authors Cited by (9) Tweetations (4) Metrics

Journals

  1. Cho J, Park J, Lee H, Jo H, Lee S, Kim H, Son Y, Kim H, Woo S, Kim S, Kang J, Pizzol D, Hwang J, Smith L, Yon D. National trends in adolescents’ mental health by income level in South Korea, pre– and post–COVID–19, 2006–2022. Scientific Reports 2024;14(1) View
  2. 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
  3. 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
  4. 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
  5. Kong J, Hong S, Lee S, Kim S, Kim S, Oh J, Jang W, Cho H, Lee S, Kang J, Son Y, Smith L, Woo S, Yon D. Association between behavioral and sociodemographic factors and high subjective health among adolescents: a nationwide representative study in South Korea. Scientific Reports 2025;15(1) View
  6. Lee H, Hwang S, Park S, Choi Y, Lee S, Park J, Son Y, Kim H, Kim S, Oh J, Smith L, Pizzol D, Rhee S, Sang H, Lee J, Yon D. Prediction model for type 2 diabetes mellitus and its association with mortality using machine learning in three independent cohorts from South Korea, Japan, and the UK: a model development and validation study. eClinicalMedicine 2025;80:103069 View
  7. Kim S, Kim H, Kim S, Lee H, Hammoodi A, Choi Y, Kim H, Smith L, Kim M, Fond G, Boyer L, Baik S, Lee H, Park J, Kwon R, Woo S, Yon D. Machine Learning–Based Prediction of Substance Use in Adolescents in Three Independent Worldwide Cohorts: Algorithm Development and Validation Study. Journal of Medical Internet Research 2025;27:e62805 View
  8. Park S, Kim K, Kim M, Jung H, Son Y, Park J, Pizzol D, Fond G, Boyer L, Sánchez G, Woo S, Yon D. Trends in adolescent violence victimization pre-, intra-, and post-COVID–19 pandemic in South Korea, 2012–2023: a nationwide cross-sectional study. Psychiatry Research 2025;348:116429 View
  9. Niu B, Wan M, Zhou Y. Development of an explainable machine learning model for predicting depression in adolescent girls with non-suicidal self-injury: A cross-sectional multicenter study. Journal of Affective Disorders 2025;379:690 View