Digital Phenotyping for Detecting Depression Severity in a Large Payor-Provider System: Retrospective Study of Speech and Language Model Performance
Overall, the findings suggest that speech may have significant potential for detection and measurement of depression severity over a variety of ages, gender, and socioeconomic categories that may enhance treatment, improve clinical decision-making, and enable truly personalized treatment recommendations.
Authors
- Doug Henry (ORCID: https://orcid.org/0000-0003-3627-9974)
- Bradley E. Karlin (ORCID: https://orcid.org/0000-0002-8184-3565)
- Ryan Anderson
- Michael Aratow (ORCID: https://orcid.org/0000-0003-4749-6413)
- Elizabeth Shriberg (ORCID: https://orcid.org/0009-0004-3779-4956)
- Michelle Hoy
- Salvatore Cieri
Institutions
- Johns Hopkins University (US)
- San Francisco Department of Public Health (US)
- Highmark Blue Cross Blue Shield (US)
Publication Details
- Journal
- JMIR AI
- Published
- 2025-03-31
- DOI
- https://doi.org/10.2196/69149
- Citations
- 3
- Primary Topic
- Mental Health via Writing
- Type
- article
- Field-Weighted Citation Impact
- 7.32