Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation

Challenge:	Existing systems rely on black-box neural networks, which lack interpretability, which is crucial in mental health contexts.
Approach:	They propose a Retrieval-augmented generation framework for Explainable depression detection that retrieves evidence from clinical interview transcripts, providing explanations for predictions.
Outcome:	The proposed framework retrieves evidence from clinical interview transcripts, providing explanations for predictions.

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Multimodality

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Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings (2022.coling-1)

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Sooji Han, Rui Mao, Erik Cambria

Responsible & Trustworthy NLP

Challenge:	Existing black-box-like deep learning methods for depression detection focus on improving classification performance, but it is impossible to explain and interpret those models that rely on state-of-the-art (SOTA) deep learning techniques.
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Challenge:	Clinical interviews are a standard method for assessing depression . however, these methods neglect the broader conversational context .
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Responsible & Trustworthy NLP

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Roque Fernández-Iglesias, Marcos Fernandez-Pichel, Mario Aragon, David E. Losada

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Xianbing Zhao, Yiqing Lyu, Di Wang, Buzhou Tang

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