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

Foundation Models and Their Applications in AI (46) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1973) Foundations of AI (56) Applications of AI (229) Machine Learning (1927) Depression and Mood Disorders; Suicide Prevention (1605) Clinical Information and Decision Making (1785) Voice Phenotyping and Vocal Biomarkers (11)

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69149, first published .
Digital Phenotyping for Detecting Depression Severity in a Large Payor-Provider System: Retrospective Study of Speech and Language Model Performance

Digital Phenotyping for Detecting Depression Severity in a Large Payor-Provider System: Retrospective Study of Speech and Language Model Performance

Digital Phenotyping for Detecting Depression Severity in a Large Payor-Provider System: Retrospective Study of Speech and Language Model Performance

Authors of this article:

Bradley Karlin1, 2 Author Orcid Image ;   Doug Henry1 Author Orcid Image ;   Ryan Anderson1 Author Orcid Image ;   Salvatore Cieri1 Author Orcid Image ;   Michael Aratow3 Author Orcid Image ;   Elizabeth Shriberg3 Author Orcid Image ;   Michelle Hoy3 Author Orcid Image
Article Authors Cited by Tweetations Metrics Author Video

Bradley Karlin   1, 2 , MSCP, MBA, PhD ;   Doug Henry   1 , PhD ;   Ryan Anderson   1 , PhD ;   Salvatore Cieri   1 , LCSW ;   Michael Aratow   3 , MD ;   Elizabeth Shriberg   3 , PhD ;   Michelle Hoy   3 , LPC

1 Highmark Health, Pittsburgh, PA, United States

2 Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States

3 Ellipsis Health, San Francisco, CA, United States

Corresponding Author: