Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews
This work addresses depression detection for clinical applications, but it is incremental as it modifies an existing GCN method for a specific task.
The authors tackled depression detection from transcribed clinical interviews by proposing a node-weighted Graph Convolutional Network to weight self-connecting edges, which outperformed vanilla GCN and prior results with an F1 score of 0.84 on two benchmark datasets.
We propose a simple approach for weighting self-connecting edges in a Graph Convolutional Network (GCN) and show its impact on depression detection from transcribed clinical interviews. To this end, we use a GCN for modeling non-consecutive and long-distance semantics to classify the transcriptions into depressed or control subjects. The proposed method aims to mitigate the limiting assumptions of locality and the equal importance of self-connections vs. edges to neighboring nodes in GCNs, while preserving attractive features such as low computational cost, data agnostic, and interpretability capabilities. We perform an exhaustive evaluation in two benchmark datasets. Results show that our approach consistently outperforms the vanilla GCN model as well as previously reported results, achieving an F1=0.84 on both datasets. Finally, a qualitative analysis illustrates the interpretability capabilities of the proposed approach and its alignment with previous findings in psychology.