Optimizing large language models for detecting symptoms of depression/anxiety in chronic diseases patient communications.

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Bibliographic Details
Title: Optimizing large language models for detecting symptoms of depression/anxiety in chronic diseases patient communications.
Authors: Kim J; Stanford Center for Digital Health, Department of Medicine, Stanford University, Stanford, CA, USA. jykim3@stanford.edu., Ma SP; Division of Hospital Medicine, Stanford School of Medicine, Stanford, CA, USA.; Technology and Digital Solutions, School of Medicine, Stanford University, Stanford, CA, USA.; Stanford Health Care, Palo Alto, CA, USA., Chen ML; Stanford Center for Digital Health, Department of Medicine, Stanford University, Stanford, CA, USA., Galatzer-Levy IR; Google LLC, Mountain View, CA, USA., Torous J; Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA., van Roessel PJ; Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Palo Alto, CA, USA., Sharp C; Division of Hospital Medicine, Stanford School of Medicine, Stanford, CA, USA.; Technology and Digital Solutions, School of Medicine, Stanford University, Stanford, CA, USA.; Stanford Health Care, Palo Alto, CA, USA., Pfeffer MA; Division of Hospital Medicine, Stanford School of Medicine, Stanford, CA, USA.; Technology and Digital Solutions, School of Medicine, Stanford University, Stanford, CA, USA.; Stanford Health Care, Palo Alto, CA, USA., Rodriguez CI; Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Palo Alto, CA, USA.; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA., Linos E; Stanford Center for Digital Health, Department of Medicine, Stanford University, Stanford, CA, USA., Chen JH; Division of Hospital Medicine, Stanford School of Medicine, Stanford, CA, USA.; Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.
Source: NPJ digital medicine [NPJ Digit Med] 2025 Sep 30; Vol. 8 (1), pp. 580. Date of Electronic Publication: 2025 Sep 30.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101731738 Publication Model: Electronic Cited Medium: Internet ISSN: 2398-6352 (Electronic) Linking ISSN: 23986352 NLM ISO Abbreviation: NPJ Digit Med Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2398-6352
DOI:10.1038/s41746-025-01969-5