Towards Explainable Multimodal Depression Recognition for Clinical Interviews

TL;DR

This paper introduces EMDRC, a new explainable multimodal depression recognition task for clinical interviews, emphasizing model interpretability and symptom summarization, with a novel dataset and a PHQ-aware multi-task learning framework.

Contribution

It proposes the EMDRC task, constructs a new dataset, and develops a PHQ-aware multimodal multi-task learning model for explainable depression recognition.

Findings

Our methods outperform baselines on the new dataset.

The model effectively summarizes symptoms and predicts depression severity.

The approach enhances interpretability in clinical depression recognition.

Abstract

Recently, multimodal depression recognition for clinical interviews (MDRC) has recently attracted considerable attention. Existing MDRC studies mainly focus on improving task performance and have achieved significant development. However, for clinical applications, model transparency is critical, and previous works ignore the interpretability of decision-making processes. To address this issue, we propose an Explainable Multimodal Depression Recognition for Clinical Interviews (EMDRC) task, which aims to provide evidence for depression recognition by summarizing symptoms and uncovering underlying causes. Given an interviewer-participant interaction scenario, the goal of EMDRC is to structured summarize participant's symptoms based on the eight-item Patient Health Questionnaire depression scale (PHQ-8), and predict their depression severity. To tackle the EMDRC task, we construct a new dataset based on an existing MDRC dataset. Moreover, we utilize the PHQ-8 and propose a PHQ-aware multimodal multi-task learning framework, which captures the utterance-level symptom-related semantic information to help generate dialogue-level summary. Experiment results on our annotated dataset demonstrate the superiority of our proposed methods over baseline systems on the EMDRC task.