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

Journal: Journal of medical Internet research
PMID: 39993291

Abstract

BACKGROUND: To address gaps in global understanding of cultural and social variations, this study used a high-performance machine learning (ML) model to predict adolescent substance use across three national datasets.

Authors

  • Soeun Kim
    Research Center for Green Fine Chemicals, Korea Research Institute of Chemical Technology, Ulsan 44412, Korea.
  • Hyejun Kim
    Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, Republic of Korea.
  • Seokjun Kim
    Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Seoul, Republic of Korea.
  • Hojae Lee
    Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, Republic of Korea.
  • Ahmed Hammoodi
    Department of Business Administration, Kyung Hee University School of Management, Seoul, Republic of Korea.
  • Yujin Choi
    Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, Republic of Korea.
  • Hyeon Jin Kim
    Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, Republic of Korea.
  • Lee Smith
    Centre for Health, Performance and Wellbeing, Anglia Ruskin University, Cambridge, UK.
  • Min Seo Kim
    Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, United States.
  • Guillaume Fond
    Aix-Marseille University, EA 3279 - Public Health, Chronic Diseases and Quality of Life - Research Unit, La Timone Medical University, 27, boulevard Jean-Moulin.
  • Laurent Boyer
    Aix-Marseille University, EA 3279 - Public Health, Chronic Diseases and Quality of Life - Research Unit, La Timone Medical University, 27, boulevard Jean-Moulin.
  • Sung Wook Baik
    College of Software and Convergence Technology, Department of Software, Sejong University, Seoul, Republic of Korea.
  • Hayeon Lee
    Department of Biomedical Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin, 17104, South Korea.
  • Jaeyu Park
    Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea.
  • Rosie Kwon
    Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, Republic of Korea.
  • Selin Woo
    Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, Republic of Korea.
  • Dong Keon Yon
    Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea.

Keywords

External Resources

Popular Topics
Recent Journals