Enhancing early depression detection with AI: a comparative use of NLP models-Reference-Cited by-同舟云学术

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Enhancing early depression detection with AI: a comparative use of NLP models

Published:2024-04-23 Issue:1 Volume:17 Page:135-143
ISSN:1882-4889
Container-title:SICE Journal of Control, Measurement, and System Integration
language:en
Short-container-title:SICE Journal of Control, Measurement, and System Integration

Author:

Hadzic Bakir¹^ORCID,Mohammed Parvez¹^ORCID,Danner Michael²^ORCID,Ohse Julia³^ORCID,Zhang Yihong⁴^ORCID,Shiban Youssef³^ORCID,Rätsch Matthias¹^ORCID

Affiliation:

1. ViSiR, Reutlingen University, Reutlingen, Germany

2. CVSSP, University of Surrey, Guildford, UK

3. Private University of Applied Sciences, Göttingen, Germany

4. College of Information Science and Technology, Donghua University, Shanghai, People's Republic of China

Funder

VwV Invest BW - Innovation II

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/18824889.2024.2342624

Reference40 articles.

1. World Health Organisation. World mental health report: Transforming mental health for all. World Health Organisation; 2022.

2. Bech P. Rating scales in depression: limitations and pitfalls. In: Dialogues in clinical neuroscience 2022.

3. Why do children and adolescents (not) seek and access professional help for their mental health problems? A systematic review of quantitative and qualitative studies

4. Mental Health Care Access Using Geographic Information Systems: An Integrative Review

5. A review of depression and suicide risk assessment using speech analysis

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