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Multi-view graph-based interview representation to improve depression level estimation.

Navneet Agarwal1, Gaël Dias2, Sonia Dollfus3,4,5

  • 1UNICAEN, ENSICAEN, CNRS, GREYC, Normandie Univ, 14000, Caen, France. navneet.agarwal@unicaen.fr.

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Summary
This summary is machine-generated.

This study introduces graph-based representations for automated depression estimation, showing superior performance over sequential models. These methods offer new insights into interview transcripts for clinical use.

Keywords:
Automatic depression level estimationGraph-based interview transcript representationInsight generationKeyword correlation graphsMulti-view architecturesSimilarity graphs

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Area of Science:

  • Computational linguistics
  • Mental health informatics
  • Machine learning for healthcare

Background:

  • Depression estimation is crucial, with most research focusing on neural networks.
  • Alternative input representations for depression detection models are underexplored.
  • Graph-based methods offer a novel perspective on analyzing textual data for mental health.

Purpose of the Study:

  • To investigate the impact of graph-based input representations on depression estimation models.
  • To compare the efficacy of graph representations against traditional sequential models.
  • To introduce multi-view architectures accounting for dialogue structure in depression analysis.

Main Methods:

  • Utilized sentence similarity and keyword correlation graphs for interview and corpus-level analysis.
  • Developed multi-view architectures processing question-answer pairs separately.
  • Applied graph-based encodings to binary classification tasks for depression estimation.

Main Results:

  • Graph-based representations demonstrated significant advantages over sequential models.
  • Achieved new state-of-the-art results on the DAIC-WOZ dataset for binary classification.
  • Multi-view graphical encodings proved beneficial for depression detection.

Conclusions:

  • Graph-based and multi-view approaches offer a powerful alternative for automated depression estimation.
  • These methods provide valuable insights and visual summaries for medical professionals.
  • The study highlights the potential of novel data representations in mental health AI.