Detecting Depression With Heterogeneous Graph Neural Network in Clinical Interview Transcript

Depression has an intense impact on individuals, yet many cases go undiagnosed. Thus, it is imperative to design an effective model for the automated diagnosis of depression. However, existing methods do not adequately capture contextual information in a clinical interview. Inspired by the depression diagnosis process, we propose a new perspective on detecting depression as a dialog information extraction task. Specifically, this article constructs a heterogeneous graph that models the participant’s depression state and uses the graph attention network to aggregate the pieces of depressive clues. In addition, we use the focal loss as a loss function for dealing with class imbalance by reshaping the standard cross-entropy loss. Experimental results demonstrate that our proposed model depression state extraction with heterogeneous graph attention neural network (DSE-HGAT) surpasses the baseline models on the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) dataset. Meanwhile, agreement analysis between our proposed model and the gold standard shows that it is moderate (k = 0. 528, p 0. 05). Overall, our model is very effective in identifying depression in the clinical interview transcript, which has the potential to assist doctors with medical conditions.