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

Mobile Health (mhealth) (3464) Depression and Mood Disorders; Suicide Prevention (1691) Natural Language Processing (917) mHealth for Data Collection and Research (1196) Diagnostic Tools in Mental Health (326) Decision Support for Health Professionals (1539) Generative Language Models Including ChatGPT (708) mHealth for Diagnosis (81)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54617, first published .
Using Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study

Using Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study

Using Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study

Authors of this article:

Daun Shin1, 2 Author Orcid Image ;   Hyoseung Kim3 Author Orcid Image ;   Seunghwan Lee3 Author Orcid Image ;   Younhee Cho2, 4 Author Orcid Image ;   Whanbo Jung2 Author Orcid Image
Article Authors Cited by (19) Tweetations (1) Metrics

Journals

  1. Mahyoub M, Dougherty K, Shukla A. Extracting Pulmonary Embolism Diagnoses From Radiology Impressions Using GPT-4o: Large Language Model Evaluation Study. JMIR Medical Informatics 2025;13:e67706 View
  2. Liu J, Gu J, Tong M, Yue Y, Qiu Y, Zeng L, Yu Y, Yang F. Evaluating the Agreement Between ChatGPT-4 and Validated Mental Health Scales in Older Adults: A Cross-Sectional Study. The American Journal of Geriatric Psychiatry 2025;33(10):1049 View
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  5. John J, Prathap B, Gupta G, Melanaturu J. Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques. Natural Language Processing Journal 2025;11:100162 View
  6. Su H, Sun Y, Li R, Zhang A, Yang Y, Xiao F, Duan Z, Chen J, Hu Q, Yang T, Xu B, Zhang Q, Zhao J, Li Y, Li H. Large Language Models in Medical Diagnostics: Scoping Review With Bibliometric Analysis. Journal of Medical Internet Research 2025;27:e72062 View
  7. Li X, Chen S, Meng M, Wang Z, Jiang H, Hao Y. Research progress and implications of the application of large language model in shared decision-making in China’s healthcare field. Frontiers in Public Health 2025;13 View
  8. Jukiewicz M. How generative artificial intelligence transforms teaching and influences student wellbeing in future education. Frontiers in Education 2025;10 View
  9. Bucher A, Egger S, Vashkite I, Wu W, Schwabe G. “It’s Not Only Attention We Need”: Systematic Review of Large Language Models in Mental Health Care. JMIR Mental Health 2025;12:e78410 View
  10. Li Y, Shao S, Milling M, Schuller B. Large language models for depression recognition in spoken language integrating psychological knowledge. Frontiers in Computer Science 2025;7 View
  11. Balan R, Gumpel T. ChatGPT clinical use in mental healthcare: A scoping review of empirical evidence (Preprint). JMIR Mental Health 2025 View
  12. Kuznetsov A. Automatic information extraction from ego-documents: a comparative analysis of the effectiveness of large language models based on the example of K.A. Berezkin's diary.. Историческая информатика 2025;(3):99 View
  13. Kim J, Ma S, Chen M, Galatzer-Levy I, Torous J, van Roessel P, Sharp C, Pfeffer M, Rodriguez C, Linos E, Chen J. Optimizing large language models for detecting symptoms of depression/anxiety in chronic diseases patient communications. npj Digital Medicine 2025;8(1) View
  14. Yulianti E, Eka Putri Y, Keliat B, Hidayanto A. Feelings behind words: A systematic review on how effective IS NLP-based assessment for mental health diagnosis in human studies. International Journal of Medical Informatics 2026;205:106129 View
  15. Parameswaran V, Bernard J, Bernard A, Deo N, Tsung S, Lyytinen K, Sharp C, Rodriguez F, Maron D, Dash R. Evaluating Large Language Models and Retrieval-Augmented Generation Enhancement for Delivering Guideline-Adherent Nutrition Information for Cardiovascular Disease Prevention: Cross-Sectional Study. Journal of Medical Internet Research 2025;27:e78625 View
  16. Weber S, Deperrois N, Heun R, Frühschütz L, Monn A, Homan S, Häfliger A, Seifritz E, Kowatsch T, Jäger L, Schultebraucks K, Gershov S, Mocellin J, Kleim B, Olbrich S. Using a fine-tuned large language model for symptom-based depression evaluation. npj Digital Medicine 2025;8(1) View

Books/Policy Documents

  1. Goloviznina V, Sergeev A, Melnichenko M, Kotelnikov E. Internet and Modern Society. View

Conference Proceedings

  1. Ge Z, Hu N, Li D, Wang Y, Qi S, Xu Y, Shi H, Zhang J. 2025 IEEE 41st International Conference on Data Engineering Workshops (ICDEW). A Survey of Large Language Models in Mental Health Disorder Detection on Social Media View
  2. Wang X, Perez A, Parapar J, Crestani F. Proceedings of the 34th ACM International Conference on Information and Knowledge Management. TalkDep: Clinically Grounded LLM Personas for Conversation-Centric Depression Screening View