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

Abstract

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.

Keywords

• artificial intelligence
• machine learning
• predictive
• algorithm
• depression
• anxiety