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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU150720" target="_blank" >RIV/00216305:26230/23:PU150720 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.isca-archive.org/interspeech_2023/burdisso23_interspeech.pdf" target="_blank" >https://www.isca-archive.org/interspeech_2023/burdisso23_interspeech.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21437/Interspeech.2023-1923" target="_blank" >10.21437/Interspeech.2023-1923</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 model- ing 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 neighbor- ing nodes in GCNs, while preserving attractive features such as low computational cost, data agnostic, and interpretability capa- bilities. We perform an exhaustive evaluation in two benchmark datasets. Results show that our approach consistently outper- forms the vanilla GCN model as well as previously reported re- sults, achieving an F1=0.84% on both datasets. Finally, a qual- itative analysis illustrates the interpretability capabilities of the proposed approach and its alignment with previous findings in psychology.

Název v anglickém jazyce

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

Popis výsledku anglicky

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 model- ing 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 neighbor- ing nodes in GCNs, while preserving attractive features such as low computational cost, data agnostic, and interpretability capa- bilities. We perform an exhaustive evaluation in two benchmark datasets. Results show that our approach consistently outper- forms the vanilla GCN model as well as previously reported re- sults, achieving an F1=0.84% on both datasets. Finally, a qual- itative analysis illustrates the interpretability capabilities of the proposed approach and its alignment with previous findings in psychology.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the Annual Conference of International Speech Communication Association, INTERSPEECH

ISBN

—

ISSN

1990-9772

e-ISSN

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Počet stran výsledku

5

Strana od-do

3617-3621

Název nakladatele

International Speech Communication Association

Místo vydání

Dublin

Místo konání akce

Dublin

Datum konání akce

20. 8. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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