Identifying Adolescent Depression and Anxiety Through Real-World Data and Social Determinants of Health: Machine Learning Model Development and Validation.

Journal: JMIR mental health
PMID: 39937988

Abstract

BACKGROUND: The prevalence of adolescent mental health conditions such as depression and anxiety has significantly increased. Despite the potential of machine learning (ML), there is a shortage of models that use real-world data (RWD) to enhance early detection and intervention for these conditions.

Authors

  • Mamoun T Mardini
    University of Florida, Gainesville, Florida, USA.
  • Georges E Khalil
    Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, 7th Floor, Suite 7000, 1889 Museum Rd, Gainesville, FL, 32611, United States, 1 7049045847.
  • Chen Bai
    Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, The 278th Baoguang Avenue, Xindu District, Chengdu, Sichuan, 610500, People's Republic of China.
  • Aparna Menon DivaKaran
    Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, 7th Floor, Suite 7000, 1889 Museum Rd, Gainesville, FL, 32611, United States, 1 7049045847.
  • Jessica M Ray
    Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, 7th Floor, Suite 7000, 1889 Museum Rd, Gainesville, FL, 32611, United States, 1 7049045847.

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