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

Reviews in Digital Mental Health (227) Natural Language Processing (644) Artificial Intelligence (1204) e-Mental Health and Cyberpsychology (1403) Methods and New Tools in Mental Health Research (425) Diagnostic Tools in Mental Health (222) Bipolar Disorders (55)

Published on in Vol 9, No 4 (2022): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35928, first published .
Natural Language Processing Methods and Bipolar Disorder: Scoping Review

Natural Language Processing Methods and Bipolar Disorder: Scoping Review

Natural Language Processing Methods and Bipolar Disorder: Scoping Review

Authors of this article:

Daisy Harvey1 Author Orcid Image ;   Fiona Lobban1 Author Orcid Image ;   Paul Rayson2 Author Orcid Image ;   Aaron Warner1 Author Orcid Image ;   Steven Jones1 Author Orcid Image
Article Authors Cited by (12) Tweetations (8) Metrics

Journals

  1. Pollock Star A, Bachner Y, Cohen B, Haglili O, O'Rourke N. Social Media Use and Well-being With Bipolar Disorder During the COVID-19 Pandemic: Path Analysis. JMIR Formative Research 2022;6(8):e39519 View
  2. Loch A, Lopes-Rocha A, Ara A, Gondim J, Cecchi G, Corcoran C, Mota N, Argolo F. Ethical Implications of the Use of Language Analysis Technologies for the Diagnosis and Prediction of Psychiatric Disorders. JMIR Mental Health 2022;9(11):e41014 View
  3. Benítez-Andrades J, García-Ordás M, Russo M, Sakor A, Fernandes Rotger L, Vidal M, Kondylakis H, Rao P, Stefanidis K. Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts. Semantic Web 2023;14(5):873 View
  4. Zhu Q, Xiong J, Peng L. College students’ mental health evaluation model based on tensor fusion network with multimodal data during the COVID-19 pandemic. Biotechnology and Genetic Engineering Reviews 2023:1 View
  5. Liang Y, Liu L, Ji Y, Huangfu L, Zeng D. Identifying emotional causes of mental disorders from social media for effective intervention. Information Processing & Management 2023;60(4):103407 View
  6. Gebremeskel H, Chong F, Heyan H. Unlock Tigrigna NLP: Design and Development of Morphological Analyzer for Tigrigna Verbs Using Hybrid Approach. SSRN Electronic Journal 2023 View
  7. Yoon M, Park J, Hur T, Hua C, Hussain M, Lee S, Choi D. Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future. International Journal of Heart Failure 2024;6(1):11 View
  8. Zhou B, Yang G, Shi Z, Ma S. Natural Language Processing for Smart Healthcare. IEEE Reviews in Biomedical Engineering 2024;17:4 View
  9. Ma S, Jiang S, Yang O, Zhang X, Fu Y, Zhang Y, Kaareen A, Ling M, Chen J, Shang C. Use of Machine Learning Tools in Evidence Synthesis of Tobacco Use Among Sexual and Gender Diverse Populations: Algorithm Development and Validation. JMIR Formative Research 2024;8:e49031 View
  10. Villarreal-Zegarra D, García-Serna J, Quispe-Callo G, Lázaro-Cruz G, Centeno-Terrazas G, Galvez-Arevalo R, Escobar-Agreda S, Dominguez-Rodriguez A, Reategui-Rivera C, Finkelstein J. Self-administered interventions based on natural language processing models for reducing depressive and anxious symptoms: Systematic review and meta-analysis (Preprint). JMIR Mental Health 2024 View

Books/Policy Documents

  1. Safa R, Edalatpanah S, Sorourkhah A. Deep Learning in Personalized Healthcare and Decision Support. View