Freely Generated Word Responses Analyzed With Artificial Intelligence Predict Self-Reported Symptoms of Depression, Anxiety, and Worry
Kjell, Katarina LU ; Johnsson, Per LU and Sikström, Sverker LU
(2021)
In Frontiers in Psychology
12.
- Abstract
Background: Question-based computational language assessments (QCLA) of mental health, based on self-reported and freely generated word responses and analyzed with artificial intelligence, is a potential complement to rating scales for identifying mental health issues. This study aimed to examine to what extent this method captures items related to the primary and secondary symptoms associated with Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) described in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). We investigated whether the word responses that participants generated contained information of all, or some, of the criteria that define MDD and GAD using symptom-based rating scales that are... (More)
Background: Question-based computational language assessments (QCLA) of mental health, based on self-reported and freely generated word responses and analyzed with artificial intelligence, is a potential complement to rating scales for identifying mental health issues. This study aimed to examine to what extent this method captures items related to the primary and secondary symptoms associated with Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) described in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). We investigated whether the word responses that participants generated contained information of all, or some, of the criteria that define MDD and GAD using symptom-based rating scales that are commonly used in clinical research and practices.
Method: Participants (N = 411) described their mental health with freely generated words and rating scales relating to depression and worry/anxiety. Word responses were quantified and analyzed using natural language processing and machine learning.
Results: The QCLA correlated significantly with the individual items connected to the DSM-5 diagnostic criteria of MDD (PHQ-9; Pearson's r = 0.30-0.60, p < 0.001) and GAD (GAD-7; Pearson's r = 0.41-0.52, p < 0.001; PSWQ-8; Spearman's r = 0.52-0.63, p < 0.001) for respective rating scales. Items measuring primary criteria (cognitive and emotional aspects) yielded higher predictability than secondary criteria (behavioral aspects).
Conclusion: Together these results suggest that QCLA may be able to complement rating scales in measuring mental health in clinical settings. The approach carries the potential to personalize assessments and contributes to the ongoing discussion regarding the diagnostic heterogeneity of depression.
(Less)
- author
- Kjell, Katarina
LU
; Johnsson, Per
LU
and Sikström, Sverker
LU
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- diagnostic criteria, major depressive disorder, generalized anxiety disorder, Measurement method, artificial intelligence, natural language processing, Machine learning method, diagnostic assessment
- in
- Frontiers in Psychology
- volume
- 12
- article number
- 602581
- publisher
- Frontiers Media S. A.
- external identifiers
-
- scopus:85108177786
- pmid:34149500
- ISSN
- 1664-1078
- DOI
- 10.3389/fpsyg.2021.602581
- language
- English
- LU publication?
- yes
- id
- f6c067a4-8336-4a3a-a899-b5db0d6b17b5
- date added to LUP
- 2021-07-08 05:28:28
- date last changed
- 2024-06-15 13:16:43
@article{f6c067a4-8336-4a3a-a899-b5db0d6b17b5,
abstract = {{<p>Background: Question-based computational language assessments (QCLA) of mental health, based on self-reported and freely generated word responses and analyzed with artificial intelligence, is a potential complement to rating scales for identifying mental health issues. This study aimed to examine to what extent this method captures items related to the primary and secondary symptoms associated with Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) described in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). We investigated whether the word responses that participants generated contained information of all, or some, of the criteria that define MDD and GAD using symptom-based rating scales that are commonly used in clinical research and practices.</p><p>Method: Participants (N = 411) described their mental health with freely generated words and rating scales relating to depression and worry/anxiety. Word responses were quantified and analyzed using natural language processing and machine learning.</p><p>Results: The QCLA correlated significantly with the individual items connected to the DSM-5 diagnostic criteria of MDD (PHQ-9; Pearson's r = 0.30-0.60, p < 0.001) and GAD (GAD-7; Pearson's r = 0.41-0.52, p < 0.001; PSWQ-8; Spearman's r = 0.52-0.63, p < 0.001) for respective rating scales. Items measuring primary criteria (cognitive and emotional aspects) yielded higher predictability than secondary criteria (behavioral aspects).</p><p>Conclusion: Together these results suggest that QCLA may be able to complement rating scales in measuring mental health in clinical settings. The approach carries the potential to personalize assessments and contributes to the ongoing discussion regarding the diagnostic heterogeneity of depression.</p>}},
author = {{Kjell, Katarina and Johnsson, Per and Sikström, Sverker}},
issn = {{1664-1078}},
keywords = {{diagnostic criteria; major depressive disorder; generalized anxiety disorder; Measurement method; artificial intelligence; natural language processing; Machine learning method; diagnostic assessment}},
language = {{eng}},
publisher = {{Frontiers Media S. A.}},
series = {{Frontiers in Psychology}},
title = {{Freely Generated Word Responses Analyzed With Artificial Intelligence Predict Self-Reported Symptoms of Depression, Anxiety, and Worry}},
url = {{http://dx.doi.org/10.3389/fpsyg.2021.602581}},
doi = {{10.3389/fpsyg.2021.602581}},
volume = {{12}},
year = {{2021}},
}