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

arXiv:2307.00920v2 Announce Type: replace
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 …

abstract arxiv clinical cs.ai cs.cl depression detection graph impact interviews modeling network node semantics show simple type