Transfer Learning for Depression Screening from Follow-Up Clinical Interview Questions

Abstract

AbstractDepression is a common mental health disorder with large social and economic consequences. It can be costly and difficult to detect, traditionally requiring hours of assessment by a trained clinical. Recently, machine learning models have been trained to screen for depression with patient voice recordings collected during an interview with a virtual agent. To engage the patient in a conversation and increase the quantity of responses, the virtual interviewer asks a series of follow-up questions. However, asking fewer questions would reduce the time burden of screening for the participant. We, therefore, assess if these follow-up questions have a tangible impact on the performance of deep learning models for depression classification. Specifically, we study the effect of including the vocal and transcribed replies to one, two, three, four, five, or all follow-up questions in the depression screening models. We notably achieve this using unimodal and multimodal pre-trained transfer learning models. Our findings reveal that follow-up questions can help increase F1 scores for the majority of the interview questions. This research can be leveraged for the design of future mental illness screening applications by providing important information about both question selection and the best number of follow-up questions.