PHQ-V/GAD-V: Assessments to Identify Signals of Depression and Anxiety from Patient Video Responses

Mental health issues are a growing problem worldwide, and their detection can be complicated. Assessments such as the Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) questionnaire can be useful tools for detecting depression and anxiety, however, due to being self-repor...

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Bibliographic Details
Published in:Applied sciences 2022-09, Vol.12 (18), p.9150
Main Authors: Grimm, Bradley, Talbot, Brett, Larsen, Loren
Format: Article
Language:English
Subjects:
algorithm
Anxiety
Anxiety disorders
artificial intelligence
Assessments
Datasets
depression
Emotions
Language
Linguistics
machine learning
Mental depression
Mental disorders
Mental health
Natural language processing
predictive
Questionnaires
Risk
Schizophrenia
Social networks
Speech
Success
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Summary:Mental health issues are a growing problem worldwide, and their detection can be complicated. Assessments such as the Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) questionnaire can be useful tools for detecting depression and anxiety, however, due to being self-reported, patients may underestimate their own risk. To address this problem, two new assessments are introduced, i.e., the PHQ-V and GAD-V, that utilize open-ended video questions adapted from the PHQ-9 and GAD-7 assessments. These video-based assessments analyze language, audio, and facial features by applying recent work in machine learning, namely pre-trained transformer networks, to provide an additional source of information for detecting risk of illness. The PHQ-V and GAD-V are adept at predicting the original PHQ-9 and GAD-7 scores. Analysis of their errors shows that they can detect depression and anxiety in even cases where the self-reported assessments fail to do so. These assessments provide a valuable new set of tools to help detect risk of depression and anxiety.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12189150