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Per Johnsson psykologi samhällsvetenskapliga institutionen

Per Johnsson

Senior lecturer

Per Johnsson psykologi samhällsvetenskapliga institutionen

Freely Generated Word Responses Analyzed With Artificial Intelligence Predict Self-Reported Symptoms of Depression, Anxiety, and Worry

Author

  • Katarina Kjell
  • Per Johnsson
  • Sverker Sikström

Summary, in English

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.

Department/s

  • Department of Psychology

Publishing year

2021

Language

English

Publication/Series

Frontiers in Psychology

Volume

12

Document type

Journal article

Publisher

Frontiers Media S. A.

Topic

  • Psychology

Keywords

  • diagnostic criteria
  • major depressive disorder
  • generalized anxiety disorder
  • Measurement method
  • artificial intelligence
  • natural language processing
  • Machine learning method
  • diagnostic assessment

Status

Published

ISBN/ISSN/Other

  • ISSN: 1664-1078