Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews

TL;DR

This paper introduces a weighted self-connection approach in Graph Convolutional Networks to improve depression detection from clinical interview transcriptions, demonstrating superior performance and interpretability.

Contribution

It presents a novel weighting scheme for self-connections in GCNs, enhancing depression classification accuracy and interpretability over existing models.

Findings

Achieved F1=0.84 on two benchmark datasets.

Outperformed vanilla GCN and previous methods.

Enhanced interpretability aligned with psychological insights.

Abstract

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.