Predicting Depression in Screening Interviews from Interactive Multi-Theme Collaboration (2025)

Abstract: Automatic depression detection provides cues for early clinical intervention by clinicians. Clinical interviews for depression detection involve dialogues centered around multiple themes. Existing studies primarily design end-to-end neural network models to capture the hierarchical structure of clinical interview dialogues. However, these methods exhibit defects in modeling the thematic content of clinical interviews: 1) they fail to explicitly capture intra-theme and inter-theme correlation, and 2) they do not allow clinicians to intervene and focus on themes of interest. To address these issues, this paper introduces an interactive depression detection framework, namely Predicting Depression in Screening Interviews from Interactive Multi-Theme Collaboration (PDIMC). PDIMC leverages in-context learning techniques to identify themes in clinical interviews and then models both intra-theme and inter-theme correlation. Additionally, it employs AI-driven feedback to simulate the interests of clinicians, enabling interactive adjustment of theme importance. PDIMC achieves absolute improvements of 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, which demonstrates the effectiveness of capturing theme correlation and incorporating interactive external feedback.

## introduction depression stands as one of the primary factors affecting individuals' mental health @xcite , exerting negative impacts on their work and daily life through its influence on cognitive processes and behavioral patterns. statistics indicate a yearon-year increase in the prevalence of depressive symptoms within populations in both china and the united states @xcite , a trend that continues to escalate with advancements in detection figure 1 : previous method only focused on modeling the sequential clinical interview dialogue information. our method learns themes from clinical interview dialogues, models both intra-theme and inter-theme correlation, and finally introduces interactive feedback to guide the model in diagnosing depression. in recent years, significant efforts have been made to address the aforementioned challenges by modeling the hierarchical structure of clinical interview dialogues. based on the type of hierarchical neural network employed, existing approaches can be broadly classified into two categories: 1) modeling the hierarchical dependencies in multi-turn question-answering @xcite @xcite . to facilitate the understanding of complex clinical interview dialogues, this approach focuses on capturing the correlation between individual question-answer pairs as well as the sequential dependencies across multiple turns. 2) capturing implicit themes @xcite . this approach relies solely on learnable parameters, modeling each question-answer pair's category as an implicit theme. to smoothly introduce themes, clinical interview dialogues often include dialogues that are unrelated to the depressive state. the former approach tends to trap the model in trivial details, while the latter fails to explicitly identify theme content. moreover, neither of these methods allows clinicians to intervene in the model to focus on themes of interest. to tackle these downsides, we introduce a novel interactive depression detection framework, namely predicting depression in screening interviews from interactive multi-theme collaboration (pdimc), which is the first depression detection framework incorporating explicit theme correlation learning and external feedback intervention. as illustrated in figure 1 , clinical interview dialogues revolve around themes such as family, work, mental, and medical. to provide a more comprehensive evaluation, we additionally introduce a virtual theme overall. based on the above observations, we first design a theme-oriented in-context learning (ticl) module to extract theme content from complex clinical interview dialogues, preventing the model from being overwhelmed by trivial details. to capture both intra-theme and inter-theme correlation, we develop a theme correlation learning (tcl) module. subsequently, we introduce a interactive theme adjustment strategy (itas), which leverages the large language model (llm) to simulate clinician feedback, emphasizing key information from the feedback to further adjust the importance of different themes. extensive experimental results on the well-known clinical interview depression dataset daic-woz demonstrate the effectiveness and superiority of our approach. our main contributions are threefold: ## methodology our goal is to learn multiple themes t i , @xmath0,work,mental,medical,overall} from multiturn clinical interview dialogues s, then model both intra-theme and inter-theme correlation (x inter i , x intra i , @xmath1. additionally, we introduce feedback simulated by the llm to imitate feedback of clinician, enabling preference learning to adjust themes. finally, we fuse the adjusted themes x f d i , @xmath2. in this section, we introduce each component of the proposed model, as illustrated in figure 2 . specifically, we first present the theme-oriented in-context learning module in section 3.1, which extracts themes from clinical interview dialogues. afterwards, we introduce the theme correlation learning module, which captures both intra-theme and inter-theme correlation in section 3.2. finally, we describe the interactive theme adjustment strategy in section 3.3, which enables the model to focus on clinicianpreferred themes for depression detection. ## conclusion in this paper, we proposed a novel interactive depression detection framework. the framework includes three components: 1) the theme-oriented in-context learning module learns interview themerelated information from multi-turn dialogues. 2) the theme correlation learning module mines correlation of intra-theme and inter-theme. 3) the interactive theme adjustment strategy introduces clinical feedback simulated by the llm to guide the model in aligning with the preferences indicated by external feedback. extensive experiments demonstrate the effectiveness and superiority of our proposed model. it not only achieves accurate depression diagnosis but also allows for clinical intervention, enabling integration of expert feedback into the diagnostic process.