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

Theme issue 2019: Semantics of Mental Health (5) Machine Learning (1501) Depression and Mood Disorders; Suicide Prevention (1392)

Published on in Vol 8, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17958, first published .
Depression Risk Prediction for Chinese Microblogs via Deep-Learning Methods: Content Analysis

Depression Risk Prediction for Chinese Microblogs via Deep-Learning Methods: Content Analysis

Depression Risk Prediction for Chinese Microblogs via Deep-Learning Methods: Content Analysis

Authors of this article:

Xiaofeng Wang1 Author Orcid Image ;   Shuai Chen2 Author Orcid Image ;   Tao Li2 Author Orcid Image ;   Wanting Li2 Author Orcid Image ;   Yejie Zhou2 Author Orcid Image ;   Jie Zheng2 Author Orcid Image ;   Qingcai Chen2 Author Orcid Image ;   Jun Yan3 Author Orcid Image ;   Buzhou Tang2 Author Orcid Image
Article Authors Cited by (36) Tweetations (1) Metrics

Journals

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  10. Gatto J, Seegmiller P, Johnston G, Preum S. Identifying the Perceived Severity of Patient-Generated Telemedical Queries Regarding COVID: Developing and Evaluating a Transfer Learning–Based Solution. JMIR Medical Informatics 2022;10(9):e37770 View
  11. Liu J, Shi M. A Hybrid Feature Selection and Ensemble Approach to Identify Depressed Users in Online Social Media. Frontiers in Psychology 2022;12 View
  12. Du X, Sun Y. Linguistic features and psychological states: A machine-learning based approach. Frontiers in Psychology 2022;13 View
  13. Atakan N, Yalçın B, Özkaya E, Küçük Ö, Öztürkcan S, Salman A, Borlu M, Şentürk N, Akman-Karakaş A, Serdaroğlu S. Atopic dermatitis diagnosis and treatment consensus report. TURKDERM 2022;56(Supple 2):86 View
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  18. Heyat M, Akhtar F, Munir F, Sultana A, Muaad A, Gul I, Sawan M, Asghar W, Iqbal S, Baig A, de la Torre Díez I, Wu K. Unravelling the complexities of depression with medical intelligence: exploring the interplay of genetics, hormones, and brain function. Complex & Intelligent Systems 2024;10(4):5883 View
  19. Rehmani F, Shaheen Q, Anwar M, Faheem M, Bhatti S. Depression detection with machine learning of structural and non‐structural dual languages. Healthcare Technology Letters 2024;11(4):218 View
  20. Yang M, Li Z, Gao Y, He C, Huang F, Chen W. Heterogeneous Graph Attention Networks for Depression Identification by Campus Cyber-Activity Patterns. IEEE Transactions on Computational Social Systems 2024;11(3):3493 View
  21. Liu Y, Ding X, Peng S, Zhang C. Leveraging ChatGPT to optimize depression intervention through explainable deep learning. Frontiers in Psychiatry 2024;15 View
  22. Montejo-Ráez A, Molina-González M, Jiménez-Zafra S, García-Cumbreras M, García-López L. A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges. Computer Science Review 2024;53:100654 View
  23. Liu Z, Wu Y, Zhang H, Li G, Ding Z, Hu B. Stimulus-Response Patterns: The Key to Giving Generalizability to Text-Based Depression Detection Models. IEEE Journal of Biomedical and Health Informatics 2024;28(8):4925 View
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Books/Policy Documents

  1. Sharma T, Panchendrarajan R, Saxena A. Deep Learning for Social Media Data Analytics. View
  2. Wongkoblap A, Vadillo M, Curcin V. Mental Health in a Digital World. View
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