Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews
| Type of publication: | Idiap-RR |
| Citation: | Burdisso_Idiap-RR-03-2023 |
| Number: | Idiap-RR-03-2023 |
| Year: | 2023 |
| Month: | 6 |
| Institution: | Idiap |
| Note: | Paper submitted to INTERSPEECH 2023 |
| Abstract: | In this work, we propose a novel 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 and control subjects. The proposed method aim to mitigate the limitation assumptions of locality and the equal importance of self-connections vs. edges to neighboring nodes in GCNs, while inheriting some attractive features such as, low computational cost, being data agnostic, and having 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 a SOTA F1=0.84% on both datasets. Finally, qualitative analysis shows an alignment between findings in psychology research and what the model learned from the data. |
| Keywords: | depression detection, Graph Neural Networks, Interpretability, limited training data, node weighted graphs |
| Projects |
Idiap |
| Authors | |
| Crossref by |
Burdisso_INTERSPEECH_2023 |
| Added by: | [ADM] |
| Total mark: | 0 |
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