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

e-Learning and Digital Medical Education (1008) Substance Abuse (573) Machine Learning (2323) Substance and Alcohol Abuse in Adolescence (141) Mental Health Surveillance and Epidemiology (108) Harm Reduction Approaches (46)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/62805, first published .
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Machine Learning–Based Prediction of Substance Use in Adolescents in Three Independent Worldwide Cohorts: Algorithm Development and Validation Study

Machine Learning–Based Prediction of Substance Use in Adolescents in Three Independent Worldwide Cohorts: Algorithm Development and Validation Study

Authors of this article:

Soeun Kim1, 2 Author Orcid Image ;   Hyejun Kim1, 3 Author Orcid Image ;   Seokjun Kim1, 4 Author Orcid Image ;   Hojae Lee1 Author Orcid Image ;   Ahmed Hammoodi5 Author Orcid Image ;   Yujin Choi1, 6 Author Orcid Image ;   Hyeon Jin Kim1, 2 Author Orcid Image ;   Lee Smith7 Author Orcid Image ;   Min Seo Kim8 Author Orcid Image ;   Guillaume Fond9 Author Orcid Image ;   Laurent Boyer9 Author Orcid Image ;   Sung Wook Baik10 Author Orcid Image ;   Hayeon Lee1, 11 Author Orcid Image ;   Jaeyu Park1, 2 Author Orcid Image ;   Rosie Kwon1 Author Orcid Image ;   Selin Woo1, 4 Author Orcid Image ;   Dong Keon Yon1, 2, 4, 12 Author Orcid Image
Article Authors Cited by (5) Tweetations Metrics

Journals

  1. Kim J. Behavioral, Psychological, and Physical Predictors of Adolescent Drug Use in South Korea: Insights Obtained Using Machine Learning. International Journal of Mental Health and Addiction 2026;24(2):1825 View
  2. Lee H, Hwang J, Yon D, Rhee S. Multimodal and Multidimensional Artificial Intelligence Technology in Obesity. Journal of Obesity & Metabolic Syndrome 2025;34(4):394 View
  3. Yu B, Cho J, Kim H, Hwang S, Hwang J, Kim S, Oh J, Lee S, Kim D, Seok J, Kim K, Lee J, Yon D, Kang W. On-scene machine learning prediction model for massive transfusion in trauma and its association with in-hospital mortality. BJS Open 2025;10(1) View
  4. Vera Cruz G, Da Rosa C, Khazaal Y. Risk and protective factors in addictive behaviors: Integrating artificial intelligence and traditional analytical methods to assess social and psychological determinants. Current Opinion in Psychiatry 2026;39(4):294 View
  5. Kim T, Kim S, Kim Y, Lee H, Hwang S, Yang S, Smith L, Hajek A, Woo S, Yon D. Machine Learning Prediction Model for Dyslipidemia and Its Association With Atherothrombotic Events in 3 Independent Cohorts From South Korea, Japan, and the United Kingdom: Algorithm Development and Validation Study. JMIR Medical Informatics 2026;14:e81130 View