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

Digital Health Reviews (3933) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (3421) Natural Language Processing (1362) Depression and Mood Disorders; Suicide Prevention (2556) Machine Learning (3392) Digital Biomarkers and Digital Phenotyping (485) Artificial Intelligence (5123)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/82686, first published .
Therapist taking notes while a distressed young man sits with his head in his hands.

Text-Based Depression Estimation Using Machine Learning With Standard Labels: Systematic Review and Meta-Analysis

Text-Based Depression Estimation Using Machine Learning With Standard Labels: Systematic Review and Meta-Analysis

Authors of this article:

Shengming Zhang1 Author Orcid Image ;   Chaohai Zhang1 Author Orcid Image ;   Jiaxin Zhang1, 2 Author Orcid Image
Article Authors Cited by (4) Tweetations (2) Metrics

Journals

  1. Davoudi Z, Roostaee M, Aghajani K. From ensembles to knowledge-augmented reasoning: Information fusion for depression detection in the generative AI era. Informatics in Medicine Unlocked 2026;64:101781 View
  2. Sun Z, Wu Y, Xu L, Hu T, Xu F, Li H. Facial Expressions, Operational Behaviors, and Textual Feedback: A Meta-Analysis on the Effectiveness of Intelligent Multimodal User Experience Evaluation. International Journal of Human–Computer Interaction 2026:1 View
  3. Palka A, Kopyciński W, Krawczyk N, Wilczkowski P, Krzysztofek N, Kamela M, Boszczyk K, Grzybowska M, Baran A, Krawczyk O. ARTIFICIAL INTELLIGENCE IN PSYCHIATRIC DIAGNOSIS AND CLINICAL DECISION-MAKING: CURRENT EVIDENCE, CLINICAL APPLICATIONS, CHALLENGES AND FUTURE DIRECTIONS. International Journal of Innovative Technologies in Social Science 2026;5(3(51)) View

Books/Policy Documents

  1. Statharakos N, Papadopoulou E, Vlontaki V, Statharakou A, Moutos D. Generation Z in a Digitally Native World. View