Predicting Depression in Screening Interviews from Interactive Multi-Theme Collaboration (2025.findings-acl)
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| Challenge: | Existing methods for depression detection do not capture intra-theme and inter-themes correlation and do not allow clinicians to focus on themes of interest. |
| Approach: | They propose an interactive depression detection framework that leverages in-context learning techniques to identify themes in clinical interviews and then models both intra-theme and inter-themes correlation. |
| Outcome: | The proposed framework achieves 12% on Recall and 35% on F1-dep. metrics compared to the previous state-of-the-art model on the depression detection dataset DAIC-WOZ. |
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| Challenge: | Existing methods to diagnose depression require time-intensive interviews, assessments, and analysis. |
| Approach: | They propose a model that analyzes interview transcripts to identify depression while jointly categorizing interview prompts into latent categories. |
| Outcome: | The proposed model outperforms baseline models and provides psycholinguistic insights about depression. |
Depression Detection in Clinical Interviews with LLM-Empowered Structural Element Graph (2024.naacl-long)
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| Challenge: | Existing methods for assessing depression only capture part of relevant elements . scarcity of participant data constrains interview modeling due to privacy concerns . |
| Approach: | They propose a structural element graph (SEGA) that transforms clinical interviews into an expertise-inspired directed acyclic graph for comprehensive modeling. |
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Mitigating Interviewer Bias in Multimodal Depression Detection: An Approach with Adversarial Learning and Contextual Positional Encoding (2025.findings-emnlp)
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| Challenge: | Clinical interviews are a standard method for assessing depression . however, these methods neglect the broader conversational context . |
| Approach: | They develop a multimodal dialogue-level transformer that captures the dynamics of dialogue within each interview . they also build an adversarial classifier with a gradient reversal layer to learn shared representations . |
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Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation (2025.findings-acl)
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| 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. |
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Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires (2022.acl-long)
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| Challenge: | Existing approaches to identify mental health conditions using social media are limited by the presence of symptoms described in a questionnaire used by clinicians. |
| Approach: | They propose to ground a model in PHQ9's symptoms to improve generalization . they also show that this approach can still perform competitively on in-domain data. |
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Probabilistic Depression Detection from Textual Time Series (2026.findings-acl)
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| Challenge: | Existing models for depression severity estimations lack uncertainty estimates and temporal interpretability. |
| Approach: | They propose a Probabilistic framework for Depression Detection from clinical interview utterance sequences that predicts PHQ-8 scores while modeling calibrated uncertainty. |
| Outcome: | The proposed framework achieves competitive performance among text-only systems and produces well-calibrated intervals. |
Leveraging Mental Health Forums for User-level Depression Detection on Social Media (2022.lrec-1)
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| Challenge: | Existing methods to detect depression on social media platforms are limited due to the vastness of social media content and the lack of linguistic features. |
| Approach: | They propose to optimize the performance of user-level depression classification to lessen the burden on computational resources. |
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DepressMind: A Depression Surveillance System for Social Media Analysis (2024.eacl-demo)
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| Challenge: | DepressMind is a tool for the analysis of social network data on depression . the tool explores multiple psychological dimensions associated with clinical depression based on the social network . |
| Approach: | They propose to use social network data to analyze clinical depression . they aim to link extracts from social networks with symptoms of the Beck Depression Inventory . |
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D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat (2022.emnlp-main)
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| Challenge: | Existing human-machine dialogue systems are not able to provide diagnostic information for depression diagnosis due to stigma associated with mental illness. |
| Approach: | They propose to construct a Chinese Dialogue Dataset for depression-diagnosis-oriented chat based on clinical depression diagnostic criteria. |
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What is Stigma Attributed to? A Theory-Grounded, Expert-Annotated Interview Corpus for Demystifying Mental-Health Stigma (2025.acl-long)
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| Challenge: | Existing resources for training neural models to finely classify mental-health stigma are limited, relying primarily on social media or synthetic data without theoretical underpinnings. |
| Approach: | They propose to use an expert-annotated corpus of human-chatbot interviews to finely classify mental-health stigma. |
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