Identifying Adolescent Depression and Anxiety Through Real-World Data and Social Determinants of Health: Machine Learning Model Development and Validation
This study highlights the potential of ML in early identification of depression and anxiety in adolescents using RWD. By leveraging RWD, health care providers may more precisely identify at-risk adolescents and intervene earlier, potentially leading to improved mental health outcomes.
Authors
- Jessica M. Ray (ORCID: https://orcid.org/0000-0003-3410-1507)
- Georges E. Khalil (ORCID: https://orcid.org/0000-0003-2870-2365)
- Mamoun Mardini (ORCID: https://orcid.org/0000-0002-5345-8811)
- Aparna Menon DivaKaran
Institutions
- Florida Museum of Natural History (US)
- Florida College (US)
Publication Details
- Journal
- JMIR Mental Health
- Published
- 2024-12-31
- DOI
- https://doi.org/10.2196/66665
- Citations
- 6
- Primary Topic
- Child and Adolescent Psychosocial and Emotional Development
- Type
- article
- Field-Weighted Citation Impact
- 3.83