Text-based Interpretable Depression Severity Modeling via Symptom Predictions

Floris Van steijn, Gizem Sogancioglu, Heysem Kaya

Research output: Contribution to conferencePaperAcademic


Mood disorders in general and depression in particular are common and their impact on individuals and society is high. Roughly 5% of adults worldwide suffer from depression. Commonly, depression diagnosis involves using questionnaires, either clinician-rated or self-reported. Due to the subjectivity in questionnaire methods and high human-related costs involved, there are ongoing efforts to find more objective and easily attainable depression markers. As is the case with recent audio, visual and linguistic applications, state-of-the-art approaches for automated depression severity prediction heavily depend on deep learning and black box modeling without explainability and interpretability considerations. However, for reasons ranging from regulations to understanding the extent and limitations of the model, the clinicians need to understand the decision making process of the model to confidently form their decisions. In this work, we focus on text-based depression severity level prediction on DAIC-WOZ corpus and benefit from PHQ-8 questionnaire items to predict the symptoms as interpretable high level features. We show that using a multi-task regression approach with state-of-the-art text-based features to predict the depression symptoms, it is possible to reach a viable test set Concordance Correlation Coefficient performance comparable to the state-of-the-art systems.
Original languageEnglish
Number of pages9
Publication statusPublished - 7 Nov 2022
Event24th ACM International Conference on Multimodal Interaction - Bengaluru, India
Duration: 7 Nov 202211 Nov 2022


Conference24th ACM International Conference on Multimodal Interaction
Abbreviated titleICMI '22
Internet address


  • AVEC'19
  • Affective Computing
  • Depression Severity Prediction
  • explainability
  • extreme learning machine
  • interpretability


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