Predicting Depression in Screening Interviews from Interactive Multi-Theme Collaboration
Authors: Xianbing Zhao, Yiqing Lyu, Di Wang, Buzhou Tang
Accepted at: acl25

Predicting Depression in Screening Interviews from Interactive Multi-Theme Collaboration

Xianbing Zhao*1,4, Yiqing Lyu1, Di Wang2, Buzhou Tang1,3,

1Harbin Institute of Technology, Shenzhen, 2Xidian University,
3Peng Cheng Laboratory, 4Jiangnan University,
zhaoxianbing_hitsz@163.com, kosmischer@stu.hit.edu.cn,
wangdi@xidian.edu.cn, tangbuzhou@gmail.com
Correspondence: tangbuzhou@gmail.com

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 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.

1 Introduction

Depression stands as one of the primary factors affecting individuals’ mental health (Wei et al., 2022), exerting negative impacts on their work and daily life through its influence on cognitive processes and behavioral patterns. Statistics indicate a year-on-year increase in the prevalence of depressive symptoms within populations in both China and the United States (Rinaldi et al., 2020), a trend that continues to escalate with advancements in detection methods. However, it remains a challenging task as it requires accurately capturing the relationships across hundreds of turns in clinical interview dialogues and predicting the depressive state (Mallo-Ragolta et al., 2019; Yang et al., 2024; Yao et al., 2024). Specifically, clinical interview dialogues are structured around multiple themes, such as family, work, and medical history. These themes exhibit inter-dependencies, and their correlations with depressive states vary in strength. As illustrated in Figure 1, clinical interview dialogues begin with natural dialogues and then evolve around multiple themes (Gratch et al., 2014; Rinaldi et al., 2020), which are closely related to the depressive state of participant.

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-

![Diagram comparing previous method and our method for modeling clinical interview dialogues](page_1012_370_388_246.png)

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.
turn question-answering (Wu et al., 2023; Anshul et al., 2024; Zhang et al., 2024). 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 (Gong and Poellabauer, 2017; Rinaldi et al., 2020). 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:

• We introduce an interactive depression detection framework for automated depression detection. To the best of our knowledge, this is the first framework that explores interactive depression detection using clinical interview data.

• We introduce a theme-oriented in-context learning technique to extract themes from clinical interview dialogues and design a theme correlation learning module to model both intra-theme and inter-theme correlation.

• We propose an interactive theme adjustment strategy, which leverages the LLM to simulate clinician feedback, dynamically adjusting the importance of theme. This enables the model to focus on clinician-preferred themes for more effective depression detection.

2 Related Work

2.1 In-context Learning

In-context Learning (ICL) (Brown et al., 2020; Dong et al., 2024) is a technique that uses few-shot in-context learning samples to guide the pre-train autoregressive LLM to produce satisfying results, without additional training or fine-tuning. The in-context learning samples are usually specifically designed for designated downstream task and can serve as auxiliary parameters attached to the model to guide the generation process. Liu et al. (2022) explored the way to better design the context sample through distance metrics, and impact of different kinds of the distance metrics like Euclidean distance and so on. Levy et al. (2023) proposed a method to reinforce generalization ability through sample diversification. Chung et al. (2022) introduced a perplexity based method for designing in-context samples. Sorensen et al. (2022) conducted cross-lingual contextual learning experiments using clustering methods. Tanwar et al. (2023) used ICL techniques to perform the alignment task of different languages.

2.2 Automatic Depression Detection

Plenty of works have been dedicated to automating the process of depression detection by implementing methods like natural language processing, machine learning, multimodal model LLM. To start with, researchers used traditional methods to tackle with the issue. For instance, Abdurrahim and Fudholi (2024) introduced a Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) based deep learning model to perform depression detecting tasks upon social media posts. Cai et al. (2023) and Misgar and Bhatia (2022) have posed time series based
LSTM to detect suicide risk. Meanwhile, there are also some works (e.g. Wang et al., 2024; Yao et al., 2024; Ying et al., 2024; Dai et al., 2021), combining feature engineering with machine learning algorithms and deep neural network methods for diagnosing mental disorder. Inspired by advances in multimodal sentiment analysis (Jia et al., 2024; Zhao et al., 2022, 2025), a growing number of researchers have applied multimodal learning approaches to the task of multimodal depression detection. Ali et al. (2024) leveraged audio and text modalities to analyze sentiment and mental health, with a method of prompt engineering. Ye et al. (2024) extracted features of audio and video respectively and used the technique of mamba to fuse them and made collaborative classification for depression detection. Jung et al. (2024) proposed HiQuE, hierarchically modeled the question and answer series in the interview dialogues for depression detection. Furthermore, LLMs like BERT, LLaMA and GPT have also been implemented by many works due to its reasoning ability. Lan et al. (2024), Shah et al. (2024), Kuzmin et al. (2024) implemented some prevailing LLMs and got decent performance on social media based dataset. Danner et al. (2023) developed a chat based method using GPT for interactive depression. Yang et al. (2024) proposed MentalLLaMA, a fine tuned version of LLaMA-2, which concentrated on interpretability of issues upon mental disorder.

To the best of our knowledge, interactive depression detection methods based on explicit theme learning have not yet been explored in the field of automated depression detection. Unlike existing related work, our model is the first to learn theme correlation and design an interactive strategy to incorporate clinical feedback for preference learning.

3 METHODOLOGY

Our goal is to learn multiple themes \( T_i, i \in \mathcal{D}_{\{family,work,mental,medical,overall\}} \) from multi-turn clinical interview dialogues \( S \), then model both intra-theme and inter-theme correlation (\( X_i^{inter}, X_i^{intra}, i \in \mathcal{D} \)). 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 \( X_i^{fd}, i \in \mathcal{D} \) to obtain final representation \( X_{final}^{fd} \) for depression prediction \( \hat{y} \). 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 clinician-preferred themes for depression detection.

3.1 Theme-oriented In-context Learning

Clinical interview dialogues are structured around multiple themes, encompassing both thematic content and trivial details. The thematic content is closely related to the depressive state. To effectively capture these theme-related contents, we design a theme-oriented in-context learning module. This module leverages in-context learning techniques and LLM to extract depression-related themes while discarding irrelevant details. The in-context learning technique guides the LLM to generate output text \( y_j \) based on the in-context template \( I \) and user input sequence \( X \). Formally, this process can be represented as:

\[
P(y_j|S, I) \triangleq p_\theta(X, I),
\]
\[
\hat{y}_j = \operatorname{argmax} P(y_j|S, I),
\]

where \( P \) represents the token probabilities and \( \hat{y}_i \) denotes the token with the highest probability. The operation of the theme-oriented in-context learning technique can be formally summarized as:

\[
T_i = p_\theta(S, I), i \in \mathcal{D},
\]
\[
\mathcal{D} = \{family, work, mental, medical, overall\},
\]

where the LLM \( p \) utilizes in-context prompt \( I \) and model parameters \( \theta \) to extract theme content \( T \) from the clinical interview dialogues, filtering out trivial details and preserving information relevant to the depressive state. The in-context template, as illustrated in Figure 2, consists of theme content (family, work, mental, medical, and overall) along with system prompts.

3.2 Theme Correlation Learning

To fully leverage the advantages of themes, we model both intra-theme and inter-theme correlation. The intra-theme correlation aims to capture how key tokens within a theme influence the depressive state, while the inter-theme correlation focuses on learning how semantic relationships across themes
Figure 2: Schematic illustration of the proposed PDIMC framework with three components. The theme-oriented in-context learning technique leverages the LLM to learn theme content from clinical interview dialogues. Theme correlation learning captures the intra- and inter-theme semantics related to depressive states. The interactive theme adjustment strategy utilizes the LLM to simulate clinical feedback, dynamically adjusting theme importance.

contribute to depression assessment. This module enhances the model’s ability to highlight key semantics within a theme while also capturing semantic dependencies across themes. Figure 2 illustrates the proposed learning framework, demonstrating how these correlations contribute to a more comprehensive depression detection process. We employ the attention mechanism to learn both intra-theme and inter-theme correlation. Formally,

\[
X^* = f_{\phi}^{corr}(X), * \in \{intra, inter\},
\]

where \( f_{\phi}^{corr}(\cdot) \) represents the correlation function with parameter \( \phi \), while \( X \) and \( X^* \) denote the input before correlation and the output after correlation, respectively. We first calculate the correlation matrix \( A \). The intra-theme correlation matrix is computed by calculating the token similarity scores, while the inter-theme correlation matrix is determined by calculating the semantic similarity across themes. Formally,

\[
A(X) = softmax(\frac{X \cdot W_Q \cdot W_K^T \cdot X^T}{\sqrt{d}}),
\]

The computational operation of the correlation function \( f_{\phi}^{corr}(\cdot) \) is as follows,

\[
f_{\phi}^{corr}(X) = A(X) \cdot X \cdot W_V,
\]

where \( A(\cdot) \) represents the correlation matrix function, while \( W_{\{Q,K,V\}} \) denotes the learnable parameter matrix of \( \phi \). We first use the pre-trained large language model \( p_\theta \) to extract the features \( X_i \) of the \( i \)-th theme \( T_i \). Formally,

\[
X_i = p_\theta(T_i) \in \mathbb{R}^{L_i \times d}, i \in \mathcal{D},
\]

The form of intra-theme correlation learning \( X_i^{intra} \) is as follows,

\[
X_i^{intra} = f_{\phi,intra}^{corr}(X_i) \in \mathbb{R}^{L_i \times d},
\]

where \( L_i \) represents the sequence length of the \( i \)-th theme, and \( d \) denotes the feature dimension. The \( \phi_{intra} \) denotes the learnable parameter matrix. After learning the token correlation semantics within the theme, we concatenate multiple themes along the sequence dimension. Formally,

\[
X_{con}^{intra} = Concat\{X_i^{intra}\}_{i=1}^{|\mathcal{D}|} \in \mathbb{R}^{(\sum L_i) \times d},
\]

immediately, we utilize the correlation learning function \( f_{\phi}^{corr}(\cdot) \) to learn the semantic of inter-theme correlation. Formally,

\[
X^{inter} = f_{\phi,inter}^{corr}(X_{con}^{intra}) \in \mathbb{R}^{(\sum L_i) \times d},
\]

where \( \phi_{inter} \) denotes the learnable parameter matrix. The intra-theme and inter-theme respectively highlight the importance of tokens within a theme and the importance of the theme itself.

3.3 Interactive Theme Adjustment Strategy

To address the issue that depression detection models cannot incorporate feedback from clinician, we have designed a customized interactive theme adjustment strategy. This strategy introduces simulated clinician feedback into the depression detection model, enabling the model to focus on the parts of interest to clinician. When incorporating external feedback information containing categories \( c \) to constrain the model output in a generative model
(Dhariwal and Nichol, 2021; Sanchez et al., 2024), the model probability likelihood \( \hat{\mathbf{P}}_{\Theta} \) consists of the model prediction \( \mathbf{P}_{\Theta} \) and the prediction of the feedback information \( \mathbf{P}_{\Phi} \), resulting in the approximated modified distribution,
\[
\hat{\mathbf{P}}_{\Theta}(x|c) \propto \mathbf{P}_{\Theta}(x) \cdot \mathbf{P}_{\Theta}(c|x)^{\gamma},
\]
where \( \gamma \) represents the constraint strength, which controls the model’s degree of focus on the constraint. By removing the auxiliary classification task without classifier guidance using Bayes rule, the same model \( \mathbf{P}_{\Theta} \) simultaneously supports both conditional and unconditional predictions to reformulate Equation (12) as: \( \mathbf{P}_{\Theta}(c|x) \propto \frac{\mathbf{P}_{\Theta}(x|c)}{\mathbf{P}_{\Theta}(x)} \).
The sampling process of classifier-free guidance can be reformulated as:
\[
\hat{\mathbf{P}}_{\Theta}(x|c) \propto \frac{\mathbf{P}_{\Theta}(x|c)^{\gamma}}{\mathbf{P}_{\Theta}(x)^{\gamma-1}},
\]
Taking the logarithm of both sides of Equation (13) results in the following form,
\[
\log \hat{\mathbf{P}}(x|c) = \gamma \log \mathbf{P}_{\Theta}(x|c) - (\gamma - 1)\log \mathbf{P}_{\Theta}(x),
\]
let \( \mathbb{P}(x) = (1-\gamma)\log \hat{\mathbf{P}}_{\Theta}(x) \), \( \beta = \frac{\gamma}{1-\gamma} \), the probability distribution of the model’s prediction can be rewritten as follows,
\[
\mathbb{P}(x) = \beta \log \mathbf{P}_{\Theta}(x|c) + \frac{\log \mathbf{P}_{\Theta}(x)}{\text{feedback prediction}} + \frac{\log \mathbf{P}_{\Theta}(x)}{\text{vanilla prediction}},
\]
when introducing conditional constraints into the model prediction, the model’s prediction is composed of the superposition of the prediction from the vanilla information and the prediction from the conditional constraint information.

Based on the above theory, we integrate the classifier-free feedback of clinician on the theme with the vanilla theme information to diagnose depression. Formally,
\[
X_i^{fd} = X_i^{inter} + w_i^{fd} X_i^{inter}, X_i^{fd} \in \mathbb{R}^{L_i \times d},
\]
The integration results for each theme are as follows:
\[
X_{final} = \sum_{i=1}^{|D|} X_i^{fd}, i \in D, w_i^{fd} \in W^{fd},
\]
where \( w_{fd} \) represents the weight of the simulated clinical feedback from the LLM. Finally, we use the Softmax function to normalize the weight of \( (1 + W^{fd}) \) and integrate the weights of multiple themes for depression detection.

3.4 Depression Detection Layer
The final fused representation \( X_{final} \) is passed through several fully connected layers and activation functions to obtain the prediction result \( \hat{y} \). We compute the cross-entropy loss between the predicted value \( \hat{y} \) and the ground truth \( y \) to optimize the model:
\[
\mathcal{L}_{CE} = - \sum_i^{|N|} y_i \log(\hat{y}_i) + (1 - y_i) \log(1 - \hat{y}_i)
\]
where \( N \) denotes the number of training samples.

4 Experiments

4.1 Experimental Settings

Datasets and Evaluation Metrics. DAIC-WOZ (Gratch et al., 2014) is a clinical interview dialogue dataset which is collected and released by the University of Southern California to help veterans back to civilian life. For metrics, we have conducted a series of experiments along with previous state-of-the-art hierarchical dialogue models (Jung et al., 2024) upon the indices of Accuracy, Precision, Recall, F1-score and G-means, and we also report the Precision, Recall, F1-score of Weighted Average version (denoted as WA*Prece., WA*Rec., WA*F1 in Table 1). Meanwhile, to compare against methods of latent theme, we reported F1 score of depression and non-depression on test set, following the previous work (Rinaldi et al., 2020) (shown in Table 2).

Implementation Details. We implemented PDIMC on Nvidia A100 GPU. Applying PyTorch library, our Adam Optimizer is configured with batch size of 32, learning rate of \( 10^{-5} \), and training epochs of 80. For pretrained large language model, we employed Qwen2.5, an open source LLM, to extract text feature.

4.2 Performance Comparison
To demonstrate the effectiveness of our poposed PDIMC, we compared our proposed methods with several baselines. These methods are hierarchical dialogue models and latent theme models respectively. The hierarchical dialogue models includes: TFN (Zadeh et al., 2017), BilSTM-1DCNN (Lin et al., 2020), MuIT (Tsai et al., 2019), MISA (Hazarika et al., 2020), Depression Vlog (D-vlog) (Yoon et al., 2022), BC-LSTM (Poria et al., 2017), ERSCL (Satt et al., 2017), ATSM (Hanai et al.,
<table>
  <tr>
    <th>Method</th>
    <th>Accuracy</th>
    <th>Precision</th>
    <th>Recall</th>
    <th>F1-Score</th>
    <th>WA*Prec.</th>
    <th>WA*Rec.</th>
    <th>WA*F1.</th>
    <th>G-Mean</th>
  </tr>
  <tr>
    <td>TFN</td>
    <td>0.81</td>
    <td>0.67</td>
    <td>0.72</td>
    <td>0.68</td>
    <td>0.84</td>
    <td>0.78</td>
    <td>0.81</td>
    <td>0.699</td>
  </tr>
  <tr>
    <td>BiLSTM-1DCNN</td>
    <td>0.78</td>
    <td>0.65</td>
    <td>0.61</td>
    <td>0.62</td>
    <td>0.77</td>
    <td>0.71</td>
    <td>0.73</td>
    <td>0.630</td>
  </tr>
  <tr>
    <td>MuLT</td>
    <td>0.84</td>
    <td>0.73</td>
    <td>0.74</td>
    <td>0.74</td>
    <td>0.81</td>
    <td>0.77</td>
    <td>0.77</td>
    <td>0.735</td>
  </tr>
  <tr>
    <td>MISA</td>
    <td>0.85</td>
    <td>0.74</td>
    <td>0.77</td>
    <td>0.74</td>
    <td>0.86</td>
    <td>0.77</td>
    <td>0.79</td>
    <td>0.755</td>
  </tr>
  <tr>
    <td>D-vlog</td>
    <td>0.84</td>
    <td>0.73</td>
    <td>0.72</td>
    <td>0.73</td>
    <td>0.82</td>
    <td>0.76</td>
    <td>0.77</td>
    <td>0.725</td>
  </tr>
  <tr>
    <td>BC-LSTM</td>
    <td>0.76</td>
    <td>0.59</td>
    <td>0.60</td>
    <td>0.59</td>
    <td>0.77</td>
    <td>0.69</td>
    <td>0.72</td>
    <td>0.595</td>
  </tr>
  <tr>
    <td>EMSDL</td>
    <td>0.80</td>
    <td>0.65</td>
    <td>0.69</td>
    <td>0.66</td>
    <td>0.69</td>
    <td>0.70</td>
    <td>0.71</td>
    <td>0.670</td>
  </tr>
  <tr>
    <td>ATSM</td>
    <td>0.81</td>
    <td>0.67</td>
    <td>0.71</td>
    <td>0.70</td>
    <td>0.85</td>
    <td>0.73</td>
    <td>0.77</td>
    <td>0.690</td>
  </tr>
  <tr>
    <td>TopicModel</td>
    <td>0.78</td>
    <td>0.63</td>
    <td>0.60</td>
    <td>0.62</td>
    <td>0.81</td>
    <td>0.71</td>
    <td>0.74</td>
    <td>0.615</td>
  </tr>
  <tr>
    <td>CADL</td>
    <td>0.83</td>
    <td>0.71</td>
    <td>0.71</td>
    <td>0.71</td>
    <td>0.85</td>
    <td>0.73</td>
    <td>0.77</td>
    <td>0.710</td>
  </tr>
  <tr>
    <td>Speechformer</td>
    <td>0.83</td>
    <td>0.70</td>
    <td>0.72</td>
    <td>0.70</td>
    <td>0.78</td>
    <td>0.76</td>
    <td>0.76</td>
    <td>0.710</td>
  </tr>
  <tr>
    <td>GRU/BiLSTM</td>
    <td>0.86</td>
    <td>0.75</td>
    <td>0.78</td>
    <td>0.75</td>
    <td>0.86</td>
    <td>0.77</td>
    <td>0.80</td>
    <td>0.765</td>
  </tr>
  <tr>
    <td>HiQuE</td>
    <td>0.87</td>
    <td>0.78</td>
    <td>0.80</td>
    <td>0.79</td>
    <td>0.85</td>
    <td>0.80</td>
    <td>0.82</td>
    <td>0.790</td>
  </tr>
  <tr>
    <th>PDIMC</th>
    <th><b>0.94</b></th>
    <th><b>0.89</b></th>
    <th><b>0.92</b></th>
    <th><b>0.90</b></th>
    <th><b>0.92</b></th>
    <th><b>0.91</b></th>
    <th><b>0.92</b></th>
    <th><b>0.905</b></th>
  </tr>
</table>

Table 1: Performance comparison between our proposed PDIMC and several baselines on the DAIC-WOZ dataset.

<table>
  <tr>
    <th>Category</th>
    <th>Model</th>
    <th>F1 dep.</th>
    <th>F1 non-dep.</th>
  </tr>
  <tr>
    <td rowspan="3">Latent Theme (Text)</td>
    <td>PR</td>
    <td>0.45</td>
    <td>0.82</td>
  </tr>
  <tr>
    <td>BERT</td>
    <td>0.44</td>
    <td>0.84</td>
  </tr>
  <tr>
    <td>JLPC</td>
    <td>0.53</td>
    <td>0.82</td>
  </tr>
  <tr>
    <td>JLPCPost</td>
    <td>0.52</td>
    <td>0.85</td>
  </tr>
  <tr>
    <th>Explicit Theme</th>
    <th>PDIMC</th>
    <th><b>0.87</b></th>
    <th><b>0.92</b></th>
  </tr>
</table>

Table 2: Performance comparison between our proposed explicit theme model and implicit theme models.

2018), TopicModel (Gong and Poellabauer, 2017), CADL (Lam et al., 2019), Speechformer (Chen et al., 2022), GRU/BiLSTM (Shen et al., 2022), Hierarchical Question Embedding Network (HiQuE) (Jung et al., 2024). The latent theme models include: PR, BERT, JLPC, JLPCPost (all reported in Rinaldi et al., 2020).

The comparison results are summarized in Table 1 and 2. By comparing the results, we could draw the following conclusions. 1) Compared to hierarchical dialogue models, our proposed method PDIMC shows remarkable improvement on all metrics. Among these baseline models, HiQuE (Jung et al., 2024) achieved the best performance in nearly every metric, except weighted average precision (WA*Prec.). For macro metrics, our proposed method achieved 11% better in Precision and F1-Score, 12% better in Recall, compared to HiQuE. 2) Compared to model of latent theme JLPC(Rinaldi et al., 2020), our model PDIMC of explicit theme achieved a 35% improvement in the F1 score of depression. The experimental results indicate that explicit themes provide more clues related to depressive states than implicit themes. This result suggests that the hierarchical dialogue model struggles to learn information related to depressive states when modeling hundreds of clinical interview dialogues. It further underscores the importance of learning themes and incorporating clinical feedback.

In addition, we explore the use of different backbone networks to extract theme-related features and incorporate various LLM to simulate clinician feedback. Notably, models utilizing Qwen2.5-7B and LLaMA3-8B both achieved an F1 score of 0.90, indicating that the framework is relatively robust to variations in textual features. However, when Qwen2.5-7B, Gemma-9B, and LLaMA3-8B are used to simulate feedback, the model attained F1 scores of 0.90, 0.79, and 0.71, respectively. This result highlights that the quality of simulated feedback from LLMs substantially affects model performance, suggesting that the reasoning capability of the LLM plays a critical role in the final prediction.

4.3 Ablation Study

To assess the effectiveness of each module we proposed, we conducted a series of decremental ablation study, removing one component once. The results are displayed in Table 3, and we are giving a specific explanation. 1) Without Single Theme. In this section we alternately removed each theme and use the rest four themes to perform prediction. According to the results, removing any theme would cause a performance drop, from 5% drop on WA*F1 of work theme to 11% of mental theme
<table>
  <tr>
    <th>Model</th>
    <th>Acc.</th>
    <th>WA*Prec.</th>
    <th>WA*Rec.</th>
    <th>WA*F1</th>
  </tr>
  <tr>
    <td>w/o family</td>
    <td>0.85</td>
    <td>0.85</td>
    <td>0.85</td>
    <td>0.85</td>
  </tr>
  <tr>
    <td>w/o work</td>
    <td>0.87</td>
    <td>0.87</td>
    <td>0.87</td>
    <td>0.87</td>
  </tr>
  <tr>
    <td>w/o mental</td>
    <td>0.81</td>
    <td>0.81</td>
    <td>0.81</td>
    <td>0.81</td>
  </tr>
  <tr>
    <td>w/o medical</td>
    <td>0.85</td>
    <td>0.85</td>
    <td>0.85</td>
    <td>0.85</td>
  </tr>
  <tr>
    <td>w/o overall</td>
    <td>0.81</td>
    <td>0.81</td>
    <td>0.81</td>
    <td>0.81</td>
  </tr>
  <tr>
    <td>w/o TCL</td>
    <td>0.78</td>
    <td>0.77</td>
    <td>0.77</td>
    <td>0.77</td>
  </tr>
  <tr>
    <td>w/o ITAS</td>
    <td>0.81</td>
    <td>0.81</td>
    <td>0.81</td>
    <td>0.81</td>
  </tr>
  <tr>
    <th>PDIMC</th>
    <th><b>0.94</b></th>
    <th><b>0.92</b></th>
    <th><b>0.91</b></th>
    <th><b>0.92</b></th>
  </tr>
</table>

Table 3: Ablation study to investigate the effect of different themes and tailored module.

and overall theme. This demonstrated the irreplaceability of each theme, as they can maximize the utilization of the input text and collaboratively predict depression predisposition. 2) Without TCL Module. We removed the Theme Correlation Learning module, which means self-attention mechanism is disabled. From the result, we can see that the performance suffers the most performance decline, about 15% on WA*F1. This manifested the validity of self-attention mechanism which learns correlation both intra-theme and inter-theme and reinforces the accuracy of model predictions. 3) Without ITAS Module. This variant removes the Interactive Theme Adjustment Strategy module, and this means no discrepancy of weights would be applied during the multi-theme fusion process. Evidently, the 11% drop on WA*F1 indicated the indispensability of considering the prioritization of each theme when fusing them together. In general, the complete version of our model PDIMC outperformed the others with module removed in depression detection tasks. This result verify the effectiveness and complementary of the three components.

4.4 Theme-oriented In-context Learning Analysis

Apart from achieving the superior performance, the key advantage of our model over other approaches is its ability to learn thematic content from complex clinical dialogues due to theme-oriented in-context learning. To this end, we carried out experiments to explore the ability of the module to extract thematic descriptions from the transcript text of the interview based on the in-context samples. Figure 3 illustrates the themes extracted from hundreds of dialogue rounds, as well as the results of simulated clinical feedback generated by the large language model. By examining Figure 3,

<table>
  <tr>
    <th colspan="2">Text Input</th>
    <th colspan="2">LLM Output</th>
  </tr>
  <tr>
    <td colspan="2">We are conducting a research concerning depression detection, this is the theme of a patient's conversation. Please, you should decide the importance of each theme in the following order: Family (1), Work (2), Mental (3), Medical (4). You should give a quantitative feedback. Form of your answer should be [1,2,3,4] or [1,2,3,4,5]. The number represents the importance of the theme in [1,2,3,4,5]. Larger number represents that the aspect is more important.</td>
    <td colspan="2">Here’s a qualitative assessment of the importance of each theme of this conversation. For example, 1, 1, 1, 4, 4, 4. Family and work are equally important. Mental and medical are also important. A comprehensive evaluation would require more in-depth information and professional clinical judgment.</td>
  </tr>
  <tr>
    <td colspan="2"><b>Family Theme</b><br>The participant has a twin brother. They get along with each other but their opposite personalities. They also have a sister they haven’t contacted in years. They haven’t seen their mother since childhood contact with her after their separation/relationship.</td>
    <td colspan="2">While strained relationships with family don’t seem to be the primary driver of the depression. The participant’s current state is characterized by a lack of motivation and interest in activities.</td>
  </tr>
  <tr>
    <td colspan="2"><b>Work Theme</b><br>The participant is diagnosed with depression and has been struggling at his job ever since. He has been feeling very low mood, hopeless, and recently felt extremely down.</td>
    <td colspan="2">The lack of detail about work makes it difficult to assess its impact on the participant’s current state. However, the lack of detail points to a larger work-related issue.</td>
  </tr>
  <tr>
    <td colspan="2"><b>Mental Theme</b><br>The participant mentions several depressive symptoms in the past, which is consistent with the diagnosis. The participant is currently medicated for high cholesterol but stopped due to side effects.</td>
    <td colspan="2">This is the most crucial theme. The participant self-reported feelings of extreme sadness and hopelessness. The current feeling of extreme sadness are strong indicators.</td>
  </tr>
  <tr>
    <td colspan="2"><b>Medical Theme</b><br>The participant mentions a history of anxiety, and interaction with a doctor who prescribed medications. The participant often experiences physical symptoms such as headaches and stomachaches. However, their current state is characterized by a lack of motivation and interest in activities.</td>
    <td colspan="2">The participant’s interactions with the doctor and coping mechanisms provide valuable context. Their desire to avoid medication and their symptoms support the diagnosis of depression.</td>
  </tr>
  <tr>
    <td colspan="2">Overall Theme</td>
    <td colspan="2">Overall (S)<br>The participant’s interactions with the doctor and coping mechanisms provide valuable context. Their desire to avoid medication and their symptoms support the diagnosis of depression.</td>
  </tr>
</table>

Figure 3: Visualization of the theme-oriented in-context learning technique illustrates the thems learned from clinical interview dialogues.

![Visualization of the theme-oriented in-context learning technique illustrates the thems learned from clinical interview dialogues.](page_1012_120_464_312.png)

we can observe that the theme-oriented in-context learning technique accurately captures content related to family, work, mental health, and medical themes within the dialogue. Furthermore, the feedback simulated by LLM aligns well with common knowledge (for more details, refer to Section 4.6). This result proved the capability of TCL module to extract valid thematic information from complex dialogues.

4.5 Theme Correlation Learning Analysis

To qualitatively validate the effectiveness of the TCL module to capture correlation both intra-theme and inter-theme, we conducted experiments and visualized the map of attention distributions. Figure 4 displays the token weights of intra-theme correlation distribution. From Figure 4, we could observe the token distribution within the

![Visualization of intra-theme attention weights.](page_1012_1042_464_312.png)

Figure 4: Visualization of intra-theme attention weights.
Figure 5: Visualization of inter-theme attention weights. (a) uses description of each single theme respectively to predict depression tendency, and we notate the predicted label upon the attention map of (b) (D for Depressed, N for Non-Depressed). Additionally, average attention scores of themes that share same and different predicted labels are calculated under the attention map in (b).

"“Mental” theme and the corresponding attention weights. Some tokens, such as "diagnosed," "depression", "extremely down" and "low mood," show a clear depressive tendency, and their corresponding attention weights are significantly higher than those of other tokens. This result indicates that intra-theme correlation learning can highlight token semantic information related to depressive states, thereby enhancing the performance of the depression detection model.

To gain the deep insights into our proposed inter-theme of theme correlation learning, we analyzed the weights of several inter-theme correlation. We obtained the following observations: 1) Themes with consistent depressive semantics have higher inter-theme correlation weights, while themes with inconsistent depressive semantics have lower correlation weights. For example, the highest similarity score among themes with consistent depressive semantics is 0.951, whereas the lowest similarity score among themes with inconsistent depressive semantics is 0.204. 2) The average weight of themes with consistent depressive semantics is higher than that of themes with inconsistent depressive semantics (0.742 vs. 0.525). 3) The inter-theme correlation weights are highly correlated with and accurately reflect depressive semantic information. These results indicate that inter-theme correlation learning effectively captures cross-theme semantic correlations, aiding the model’s decision-making process and ultimately improving prediction accuracy.

Figure 6: Visualization of the weights of each theme before and after applying the interactive theme adjustment strategy.

4.6 Interactive Theme Adjustment Strategy Analysis

To validate the effectiveness of the interactive theme adjustment strategy, we compared the weights obtained through inter-theme correlation learning with those learned through the interactive theme adjustment strategy. Specifically, we first used the LLM to simulate clinical doctor feedback by scoring each theme, and then converted the scores into weights to intervene in the model’s prediction. From Figure 6, we can observe that the LLM’s scoring of theme importance is highly intuitive, such as the "Mental" theme receiving a high weight due to its direct correlation with depressive states. This result indicates that using the LLM to simulate clinician feedback is accurate.

From Figure 6, we also could see that inter-theme correlation learning could, to some extent, capture semantic information related to depression, such as the higher weight of the "Mental" theme compared to other themes. However, the distinction is still not sufficiently clear, which may lead to incorrect predictions. After applying the interactive theme adjustment strategy, the weight of the "Mental" theme becomes significantly higher than that of other themes, leading to the correct prediction. This is because, during the multi-theme fusion phase, the distinction between themes is not clear enough, reducing the influence of key themes on the prediction results. These results demonstrate the importance of introducing simulated clinician feedback through the LLM, as it highlights key theme information to guide the model towards making accurate predictions.
5 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 theme-related 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.

Limitations

There are three limitations in this study. First, although our approach achieves outstanding performance on depression detection datasets, it only utilizes textual data from clinical interview dialogues without incorporating multimodal information. Integrating multimodal data could potentially further enhance the model’s performance. Second, in designing the interactive depression detection framework, we use the large language model to simulate clinical feedback as external guidance. However, the professional reliability of this simulated feedback may require further evaluation. Finally, this method lacks validation in real-world clinical settings.

Acknowledgments

This study is partially supported by National Key R&D Program of China (2023YFC3502900), National Natural Science Foundation of China (62276082), Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS16/E09/22), Major Key Project of PCL (PCL2021A06), Shenzhen Soft Science Research Program Project (RKX20220705152815035), Shenzhen Science and Technology Research and Development Fund for Sustainable Development Project (GXWD20231128103819001, No.KCXZFZ20201221173613036, 20230706140548006), and the Fundamental Research Fund for the Central Universities (HIT.DZJJ.2023117), and the Outstanding Youth Science Foundation of Shaanxi under Grant 2025JC-JCQN-083, and the Key Research and Development Program of Shanxi Province under Grant 2025CY-YBXM-047.

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Reviewer paper summaries: This paper presents PDIMC, a novel framework for automatic depression detection from clinical interview dialogues. The approach addresses limitations in existing methods by explicitly modeling theme correlations and incorporating interactive feedback. The framework consists of three main components: theme-oriented in-context learning to extract themes from dialogues, theme correlation learning to model intra-theme and inter-theme relationships, and an interactive theme adjustment strategy that leverages LLMs to simulate clinical feedback. The method demonstrates significant performance improvements over previous state-of-the-art models on the DAIC-WOZ dataset.
Summary of strengths: The paper introduces a comprehensive and innovative approach to depression detection that explicitly models theme correlations and incorporates interactive feedback, addressing limitations in existing hierarchical dialogue models.
Summary of weaknesses: 1. While the LLM-simulated clinical feedback shows promising results, the professional reliability of this simulated feedback may require further validation with actual clinician input.
2. The evaluation is limited to a single dataset (DAIC-WOZ), raising questions about how well the approach would generalize to other clinical settings or interview formats.
Comments, suggestions and typos: See above.
Overall assessment scores: 3.5