Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Question-based computational language approach outperform ratings scale in discriminating between anxiety and depression

Tabesh, Mona LU ; Mirström, Mariam LU orcid ; Böhme, Rebecca Astrid LU ; Lasota, Marta LU ; Javaherian, Yousef ; Agbotsoka-Guiter, Thibaud and Sikström, Sverker LU orcid (2025) In Journal of Anxiety Disorders 112.
Abstract

Major Depression (MD) and General Anxiety Disorder (GAD) are the most common mental health disorders, which typically are assessed quantitatively by rating scales such as PHQ-9 and GAD-7. However, recent advances in natural language processing (NLP) and machine learning (ML) have opened up the possibility of question-based computational language assessment (QCLA). Here we investigate how accurate open-ended questions, using descriptive keywords or autobiographical narratives, can discriminate between participants that self-reported diagnosis of depression and anxiety, or health control. The results show that both language and rating scale measures can discriminate well, however, autobiographical narratives discriminate best between... (More)

Major Depression (MD) and General Anxiety Disorder (GAD) are the most common mental health disorders, which typically are assessed quantitatively by rating scales such as PHQ-9 and GAD-7. However, recent advances in natural language processing (NLP) and machine learning (ML) have opened up the possibility of question-based computational language assessment (QCLA). Here we investigate how accurate open-ended questions, using descriptive keywords or autobiographical narratives, can discriminate between participants that self-reported diagnosis of depression and anxiety, or health control. The results show that both language and rating scale measures can discriminate well, however, autobiographical narratives discriminate best between healthy and anxiety (ϕ = 1.58), as well as healthy and depression (ϕ = 1.38). Descriptive keywords, and to a certain extent autobiographical narratives, also discriminate better than summed scores of GAD-7 and PHQ-9 (ϕ=0.80 in discrimination between anxiety and depression), but not when individual items of these scales were analyzed by ML (ϕ=0.86 and ϕ=0.91 in item-level analysis of PHQ-9 and GAD-7, respectively). Combining the scales consistently elevated the discrimination even more (ϕ=1.39 in comparison between depression and anxiety), both in item-level and sum-scores analyses. These results indicate that QCLA measures often, but not in all cases, are better than standardized rating scales for assessment of depression and anxiety. Implication of these findings for mental health assessments are discussed.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
AI, Anxiety, Assessment, Depression, Language, Rating scales
in
Journal of Anxiety Disorders
volume
112
article number
103020
publisher
Elsevier
external identifiers
  • pmid:40279835
  • scopus:105003227657
ISSN
0887-6185
DOI
10.1016/j.janxdis.2025.103020
language
English
LU publication?
yes
id
dcda5710-4721-49c5-8c37-cd67afca3b25
date added to LUP
2025-08-01 09:58:46
date last changed
2025-08-02 02:44:29
@article{dcda5710-4721-49c5-8c37-cd67afca3b25,
  abstract     = {{<p>Major Depression (MD) and General Anxiety Disorder (GAD) are the most common mental health disorders, which typically are assessed quantitatively by rating scales such as PHQ-9 and GAD-7. However, recent advances in natural language processing (NLP) and machine learning (ML) have opened up the possibility of question-based computational language assessment (QCLA). Here we investigate how accurate open-ended questions, using descriptive keywords or autobiographical narratives, can discriminate between participants that self-reported diagnosis of depression and anxiety, or health control. The results show that both language and rating scale measures can discriminate well, however, autobiographical narratives discriminate best between healthy and anxiety (ϕ = 1.58), as well as healthy and depression (ϕ = 1.38). Descriptive keywords, and to a certain extent autobiographical narratives, also discriminate better than summed scores of GAD-7 and PHQ-9 (ϕ=0.80 in discrimination between anxiety and depression), but not when individual items of these scales were analyzed by ML (ϕ=0.86 and ϕ=0.91 in item-level analysis of PHQ-9 and GAD-7, respectively). Combining the scales consistently elevated the discrimination even more (ϕ=1.39 in comparison between depression and anxiety), both in item-level and sum-scores analyses. These results indicate that QCLA measures often, but not in all cases, are better than standardized rating scales for assessment of depression and anxiety. Implication of these findings for mental health assessments are discussed.</p>}},
  author       = {{Tabesh, Mona and Mirström, Mariam and Böhme, Rebecca Astrid and Lasota, Marta and Javaherian, Yousef and Agbotsoka-Guiter, Thibaud and Sikström, Sverker}},
  issn         = {{0887-6185}},
  keywords     = {{AI; Anxiety; Assessment; Depression; Language; Rating scales}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Journal of Anxiety Disorders}},
  title        = {{Question-based computational language approach outperform ratings scale in discriminating between anxiety and depression}},
  url          = {{http://dx.doi.org/10.1016/j.janxdis.2025.103020}},
  doi          = {{10.1016/j.janxdis.2025.103020}},
  volume       = {{112}},
  year         = {{2025}},
}