Abstract
BackgroundQuestion-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.MethodParticipants (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.ResultsThe 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).ConclusionTogether 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.
Highlights
Closed-ended rating scales are commonly used in clinical practice and research to assess the type and severity of mental health issues [e.g., the Patient Health Questionnaire-9 (PHQ-9); Kroenke et al, 2001, and the Generalised Anxiety Disorder Scale-7 (GAD-7); Spitzer et al, 2006]
The Semantic Hypothesis To further understand the relationship between word responses and rating scales, we examined the correlations to individual items using semantic similarity scales, language-trained scales, and language-predicted valence scales
Spearman rho was applied for the Generalized Anxiety Disorder (GAD)-7 and the PHQ-9 and Pearson’s r was applied for the PSWQ-8
Summary
Closed-ended rating scales are commonly used in clinical practice and research to assess the type and severity of mental health issues [e.g., the Patient Health Questionnaire-9 (PHQ-9); Kroenke et al, 2001, and the Generalised Anxiety Disorder Scale-7 (GAD-7); Spitzer et al, 2006] These rating scales require the respondent to rate their agreement with predefined items designed to target the construct/disorder being measured. This study aimed to further investigate the QCLA method by examining to what extent it captures individual items related to the primary and secondary symptoms associated with mental health aspects described in the DSM using the PHQ-9 (Kroenke et al, 2001) and the GAD-7 (Spitzer et al, 2006) These rating scales are designed to target the DSM criteria for Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD; American Psychological Association [APA], 2013). 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
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