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

Depression and Mood Disorders; Suicide Prevention (1691) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2114) Mental Health Surveillance and Epidemiology (95) Mental Health Issues in Adolescence (637) Diagnostic Tools in Mental Health (326) Anxiety and Stress Disorders (1330)

Published on in Vol 12 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66665, first published .
Identifying Adolescent Depression and Anxiety Through Real-World Data and Social Determinants of Health: Machine Learning Model Development and Validation

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

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

Authors of this article:

Mamoun T Mardini1 Author Orcid Image ;   Georges E Khalil1 Author Orcid Image ;   Chen Bai1 Author Orcid Image ;   Aparna Menon DivaKaran1 Author Orcid Image ;   Jessica M Ray1 Author Orcid Image
Article Authors Cited by (6) Tweetations Metrics

Journals

  1. Koushal H, Kaur R, Dhaliwal C. Machine Learning for Mental Health: Assessing Teen Depression and Anxiety Risk Factors. Cureus Journal of Computer Science 2025 View
  2. Bormpotsis C, Anagnostouli M, Sedky M, Jelastopulu E, Patel A. Mobile Mental Health Screening in EmotiZen via the Novel Brain-Inspired MCoG-LDPSNet. Biomimetics 2025;10(9):563 View
  3. Raihana Z, Kader M, Islam M, Bornee F, Mondal M, Chowdhury M, Billah B. Factors associated with the presence of anxiety and depression symptoms in rural hypertensive adults in Bangladesh: leveraging extreme gradient booster machine learning algorithm. Frontiers in Psychology 2025;16 View
  4. Ali M, Ali S, Abbas Q, Abbas Z, Lee S. Artificial intelligence for mental health: A narrative review of applications, challenges, and future directions in digital health. DIGITAL HEALTH 2025;11 View

Conference Proceedings

  1. AbdElminaam D, Amr H, Nasser J, Mohamed M, Ahmed S. 2025 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC). Mining Depression Indicators : Forecasting Depression Risk Using Ensemble and Baseline Machine Learning Methods View
  2. Kaushik D, Yadavalli R. 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON). Federated Explainable Mental Health Analytics (FEMHA): A Sustainable Framework for SDG-Aligned Risk Prediction and Emerging Challenges View