PHQ-9 Based Depression Detection Using Text with Multi-Task DeBERTa Model

Depression is among the most prevalent mental health disorders worldwide, affecting more than 280 million individuals globally according to the World Health Organization (WHO).[1] It significantly impairs emotional well-being, cognitive functioning, academic performance, and professional productivity. If left undetected and untreated, depression can progress to more severe psychiatric conditions, including anxiety disorders and suicidal ideation. Early detection is therefore critical for enabling timely intervention and minimizing long-term adverse outcomes. The Patient Health Questionnaire-9 (PHQ-9) is a widely adopted standardized clinical instrument for assessing depression severity.[2] It consists of nine items corresponding to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria for major depressive disorder. Each item is scored on a 0–3 scale, yielding a total score ranging from 0 to 27, which maps to five severity categories: minimal (0–4), mild (5–9), moderate (10–14), moderately severe (15–19), and severe (20–27). Despite its clinical validity, the PHQ-9 requires structured questionnaire administration and professional interpretation, creating barriers to accessibility, particularly in low-resource settings and among individuals with limited access to mental healthcare. With the rapid expansion of digital communication platforms, individuals increasingly express their emotional states, personal struggles, and mental health concerns through free-form written text, including social media posts, online forums, and messaging applications. This has created a unique opportunity for natural language processing (NLP) and deep learning techniques to enable automated, scalable, and nonintrusive depression screening by analyzing the linguistic content of user-generated text.[3]

Existing transformer-based approaches for depression detection have focused predominantly on binary classification tasks (depressed vs. non-depressed) using social media data. However, few studies have jointly addressed clinically relevant continuous PHQ-9 score estimation and categorical severity classification from free-form user text. Furthermore, most prior work relies on structured clinical notes or multimodal inputs, limiting their applicability to general-population digital platforms. This paper addresses these gaps by proposing an end-to-end multi-task deep learning framework that employs DeBERTa-V3, a disentangled-attention transformer model, to simultaneously predict the PHQ-9 score and classify depression severity from user-entered free-form text. The system is trained and evaluated on the PHQ-TextSet (PHQ-9 Text Assessment Dataset), a purpose-built PHQ-9-aligned textual dataset containing 3,235 annotated samples, constructed by the authors. The trained model is deployed using FastAPI, enabling real-time inference suitable for integration into digital mental health platforms and telemedicine systems. The remainder of this paper is organized as follows. Section 2 reviews the related literature on text-based depression detection and PHQ-9 assessment. Section 3 presents the proposed multi-task DeBERTa architecture. Section 4 describes the learning algorithm and mathematical formulation. Section 5 presents the experimental setup, dataset description, and results. Section 6 provides a comparative analysis with existing approaches. Section 7 concludes the paper with limitations and future directions.

Early research on automated depression detection predominantly employed traditional machine learning methods combined with handcrafted linguistic features. Studies have leveraged support vector machines (SVMs),[4] Naïve Bayes classifiers, and logistic regression[5] on text corpora derived from social media platforms and online forums. Pennebaker et al. [6] demonstrated through the Lingistic Inquiry and Word Count (LIWC) framework that depressed individuals exhibit characteristic linguistic patterns, including elevated first-person pronoun usage and increased negative affect vocabulary. These findings established the theoretical foundation for text-based depression analysis. However, such feature engineering approaches suffer from limited contextual understanding and poor generalizability across domains. Research on automated PHQ-9-based assessments has explored structured questionnaire responses, electronic health records (EHRs), and clinical text to predict PHQ-9 scores or symptom severity.[7] Alves et al. [8] proposed a machine learning model that estimates PHQ-9 symptom severity from clinical notes, reporting an area under the curve (AUC) of approximately 0.81. While this demonstrated the feasibility of PHQ-9 prediction from text, their approach relied exclusively on structured clinical documentation and addressed only single-task severity prediction, limiting its applicability to general-population real-time screening. Transformer-based language models have substantially advanced the state of the art in NLP-based mental health assessment, building upon earlier sequence models such as LSTMs[9] and deep recurrent networks.[10] Devlin et al. [11] introduced BERT (Bidirectional Encoder Representations from Transformers), which provides deeply contextualized token representations through masked language modeling. BERT-based architectures have been successfully applied to depression detection from social media text, demonstrating significant accuracy improvements over traditional NLP methods.[12] He et al. [13] proposed DeBERTa (Decoding-Enhanced BERT with Disentangled Attention), which separately encodes content and positional information, yielding superior contextual modeling capabilities that are particularly relevant for emotionally nuanced mental health text.

Multi-task learning (MTL) has emerged as a promising paradigm for mental health NLP because it exploits the inherent correlations among related symptom indicators. Compared with their single-task counterparts, MTL frameworks applied to depression-related tasks have demonstrated improved generalizability and robustness because they share learned representations across correlated objectives.[14,15] Ye et al.[16] explored multimodal depression detection combining audio and textual features; Tsai et al.[17] further extended this method with multimodal transformers for unaligned sequences, achieving promising results; however, such approaches required specialized hardware and structured input modalities, limiting scalability. Trotzek et al. [18] utilized neural networks combined with linguistic metadata for early detection of depression from text sequences and demonstrated strong performance in binary classification tasks. Despite these advances, the research literature reveals a persistent gap: few systems simultaneously perform clinically calibrated PHQ-9 score regression and categorical severity classification from unstructured, free-form user text. Moreover, most existing systems do not address real-time deployment, confidence-aware outputs, or integration with digital health platforms. The proposed system connects these gaps by combining DeBERTa-V3 with a multi-task learning architecture trained on the PHQ-TextSet dataset, enabling automated, real-time, and clinically relevant depression screening.

3.1 Overview

The proposed system is a text-based depression detection architecture designed to predict both the PHQ-9 assessment score and the corresponding depression severity level from user-entered free-form text. The architecture follows an end-to-end deep learning pipeline that integrates text preprocessing, transformer-based contextual encoding and multi-task learning to enable clinically aligned, real-time depression screening. The core of the system is a pretrained DeBERTa-V3-base transformer encoder, which generates rich semantic representations from natural language input and supports the simultaneous optimization of regression and classification objectives. The architecture is specifically designed to operate on unstructured user-provided text, without requiring structured questionnaires or clinical annotations at inference time. Fig. 1 shows system architecture for proposed system.

Fig. 1: System architecture: End-to-end pipeline from free-form text to multi-task PHQ-9 outputs.

3.2 Module-wise explanation

The architecture comprises four interconnected modules: a text preprocessing module, a transformer encoder layer, a multitask learning layer and an output layer. Data flow sequentially from the raw text input through tokenization, contextual embedding, task-specific prediction and output generation, as illustrated in Fig. 2. This modular design ensures scalability, interpretability and extensibility for future multimodal integration.

Fig. 2: Data processing and inference pipeline for PHQ-9 prediction from free-form text.

3.2.1 Text preprocessing module

User-generated text is inherently noisy and contains informal language, spelling variations, incomplete sentences and emotionally charged expressions. The preprocessing module employs the DeBERTa SentencePiece tokenizer, which applies subword-level tokenization to effectively handle out-of-vocabulary terms and morphologically complex words frequently observed in mental health-related text. Input sequences are standardized through padding (appending special tokens to bring sequences to the maximum length) and truncation (clipping sequences exceeding 256 tokens). This standardization ensures uniform input dimensions for efficient batch processing. The use of subword tokenization helps the model handle rare vocabulary, such as uncommon or newly formed words (e.g., “overthinking”, “unmotivated”, “worthlessness”), and domain-specific terminology, such as mental health-related expressions (e.g., “loss of interest”, “feeling hopeless”, “low energy”), which are commonly used to describe depressive symptoms.

3.2.2 Transformer encoder layer

The transformer encoder layer constitutes the core semantic processing component of the architecture. DeBERTa-V3-base employs a disentangled attention mechanism that separately encodes token content and positional information, in contrast to standard BERT-style models that combine both in a single representation. This disentanglement enables the model to better capture nuanced semantic relationships between contextually distant tokens, which is particularly important for understanding emotional context and symptom-related expressions in free-form text. The tokenized input is processed through 12 transformer layers, each applying multi-head self-attention and feed-forward transformations. The output is a collection of contextualized token embeddings, from which the [CLS] token representation (H₀) is extracted as a 768-dimensional global semantic summary of the entire input sequence. This representation encodes both syntactic structure and affective content, forming the shared feature vector for downstream multi-task prediction.

3.2.3 Multi-task learning layer

The multi-task learning layer enables simultaneous optimization of two clinically correlated tasks: PHQ-9 score regression and depression severity classification. The shared [CLS] representation H₀ is fed into two parallel task-specific prediction heads.

The regression head applies a linear transformation to H₀ to produce a scalar PHQ-9 score prediction:

ŷ_phq = W_r · H₀ + b_r,   W_r ∈ ℝ^(1×768), b_r ∈ ℝ     (1)

The output is denormalized from [0,1] to [0,27] for the clinical interpretation.

The classification head applies a linear transformation followed by softmax activation to produce a probability distribution over five severity classes:

ŷ_sev = Softmax(W_c · H₀ + b_c),   W_c ∈ ℝ^(5×768), b_c ∈ ℝ⁵     (2)

The predicted severity label is the argmax of this distribution. The joint training objective minimizes the combined multi-task loss:

L_total = L_MSE + λ · L_CE     (3)

where LMSE is the mean squared error regression loss, LCE is the cross-entropy classification loss, and λ = 1.0 (equal task weighting in the baseline configuration). This joint optimization encourages the model to learn a shared representation that benefits both tasks, improving generalization and consistency between predicted scores and severity labels.

3.2.4 Output layer

The output layer aggregates and presents the three final predictions: The predicted PHQ-9 score is a continuous value in [0, 27], and the predicted depression severity class (minimal, mild, moderate, moderately severe, or severe) and the confidence score are computed as the maximum softmax probability from the classification head:

Confidence = max(ŷ_sev)     (4)

The confidence score provides a measure of prediction reliability, supporting ethical and transparent clinical usage by enabling practitioners to calibrate their trust in the automated assessment.

4.1 Algorithm

Algorithm 1 summarizes the complete inference procedure of the proposed multi-task depression detection system:

4.2 Mathematical formulation

4.2.1 Input Representation

Let

T = (w₁, w₂, …, w_n)     (5)

denote the input text sequence of n subword tokens generated by the SentencePiece tokenizer. The tokenized sequence is prepended with a special [CLS] token and appended with a [SEP] token, resulting in the following input sequence:

X = {[CLS], w₁, w₂, …, w_n, [SEP]}     (6)

4.2.2 Contextual Encoding

H = DeBERTa(X)     (7)

The DeBERTa encoder maps X to a sequence of contextual embeddings:

H = DeBERTa(X) = (H₀, H₁, …, H_n)     (8)

where H₀ ∈ ℝ768 is the [CLS] representation used as the global semantic feature vector for both downstream tasks.

4.2.3 PHQ-9 score normalization and regression

The PHQ-9 score is normalized to the interval [0,1] as follows:

y_norm = y_phq / 27     (9)

The regression head predicts the following:

ŷ_phq = W_r · H₀ + b_r     (10)

The regression loss is the mean squared error:

L_MSE = (1/N) × Σ(y_norm − ŷ_phq)²     (11)

4.2.4 Severity Classification

The classification head produces a probability distribution over severity classes:

ŷ_sev = Softmax(W_c · H₀ + b_c) ∈ ℝ⁵     (12)

The predicted severity class is obtained as follows:

Severity = argmax(ŷ_sev)     (13)

The classification loss is computed using cross-entropy:

L_CE = −Σ y_true × log(ŷ_sev)     (14)

4.2.5 Combined multi-task loss

L_total = L_reg + L_cls     (15)

The overall training objective combines regression and classification losses:

L_total = L_MSE + λ · L_CE     (16)

where λ = 1.0. The model parameters are updated using AdamW optimization with weight decay to minimize Ltotal over the training set.

4.3 Evaluation metrics

The classification performance is evaluated using standard metrics, including the accuracy, precision, recall, and F1 score. The accuracy is defined as follows:

Accuracy = (TP + TN) / (TP + TN + FP + FN)     (17)

Precision = TP / (TP + FP)     (18)

Recall = TP / (TP + FN)     (19)

F1-Score = 2 × (Precision × Recall) / (Precision + Recall)     (20)

The regression performance is evaluated using the mean absolute error (MAE) and root mean square error (RMSE). The MAE is defined as follows:

MAE = (1/N) × Σ|y − ŷ|     (21)

Root Mean Squared Error:

RMSE = √((1/N) × Σ(y − ŷ)²)     (22)

Discriminative ability was assessed using the ROC-AUC curve. The confidence score is computed as max(ŷsev) from the softmax output.

5.1 Dataset description

The PHQ-TextSet (PHQ-9 Text Assessment Dataset) is an original dataset constructed by the authors to support PHQ-9-aligned text-based depression screening. It comprises 3,235 annotated samples, each containing free-form textual responses mapped to all nine PHQ-9 symptom domains. For each sample, the responses are aggregated into a unified textual representation, enabling the model to capture holistic depressive symptom patterns rather than isolated indicators. The PHQ-TextSet dataset was constructed to simulate realistic patient responses aligned with the PHQ-9 symptom criteria. Each sample represents a combination of textual expressions corresponding to the nine PHQ-9 questions, including symptoms such as low mood, fatigue, sleep disturbances, loss of interest, and feelings of worthlessness. To generate the dataset, a template-based and augmentation-driven approach was employed. For each PHQ-9 symptom category, multiple semantically diverse sentence patterns were manually designed to reflect varying levels of severity (e.g., minimal to severe). These templates were further expanded using paraphrasing techniques and lexical variation to introduce diversity in expression. The individual symptom-level texts were then aggregated into a single free-form paragraph to simulate natural user input. Each generated sample was assigned a PHQ-9 score by summing the corresponding symptom severity levels and mapping them to one of the five standard severity categories. Although the dataset is synthetic, care was taken to ensure linguistic realism and clinical consistency with the PHQ-9 guidelines. This approach enables controlled experimentation while preserving alignment with established psychological assessment frameworks. Each sample is annotated with a numerical PHQ-9 score (range: 0–27) and a categorical severity label following the standard PHQ-9 classification thresholds. The dataset encompasses five severity classes: minimal, mild, moderate, moderately severe, and severe. The class distribution is approximately balanced across all categories, with detailed statistics presented in Table 1.

5.2 Hardware and software configuration

All the experiments were conducted using Python 3.10 and PyTorch 2.0. The DeBERTa-V3-based pretrained model was loaded using the Hugging Face Transformers library (v4.35). Data preprocessing, splitting, and evaluation metrics were implemented using Scikit-learn 1.3. Model training was performed on Google Colab Pro with an NVIDIA Tesla T4 GPU with 16 GB of VRAM, enabling efficient parallel computation. The AdamW optimizer was used with a learning rate of 2×10⁻⁵ and a weight decay of 0.01 to ensure stable convergence. The complete hyperparameter configuration used during model training is summarized in Table 2. A linear learning rate warmup was applied over the first 10% of training steps to improve training stability. The model was trained for 8 epochs with a batch size of 8. After training, the model was deployed as a RESTful API using FastAPI (v0.103) to enable real-time inference and integration with external applications.

5.3 Data split and stratified sampling

The PHQ-TextSet dataset was partitioned into training (80%) and validation (20%) sets using stratified random sampling, preserving the class distribution of all five severity levels across both splits. Stratified splitting is essential in mental health datasets to prevent class imbalance from biasing the evaluation. The training set (2,588 samples) was used exclusively for model parameter optimization, while the validation set (647 samples) was reserved for performance evaluation on unseen data.

Table 1: PHQ-Textset dataset statistics and label distribution.

Table 2: Model hyperparameter configuration.

5.4 Model training and convergence

The multi-task DeBERTa model was fine-tuned on the PHQ-TextSet training set using the AdamW optimizer with the combined loss function as specified in Equation (16). The training and validation loss curves were monitored across epochs to evaluate learning stability and detect overfitting. As illustrated in Fig. 3, both training and validation loss decrease consistently across epochs, indicating stable and effective learning. The training loss decreased significantly from an initial value of 0.4459 to a final value of 0.0355 over 8 epochs, demonstrating that the model successfully learned meaningful patterns from the training data. Similarly, the validation loss decreased from 0.0944 to 0.0051, confirming the strong generalizability of the results to unseen data. Although minor fluctuations are observed in the validation loss during intermediate epochs, the overall trend remains downward. The relatively small gap between training and validation loss throughout the training process indicates that the model does not suffer from overfitting or underfitting. The smooth convergence of both loss curves demonstrates that the model achieves stable optimization and effectively balances the regression and classification objectives in the multi-task learning framework.

5.5 Performance evaluation

The performance of the proposed system was evaluated using classification metrics (accuracy, precision, recall, and F1 score) for severity prediction and regression metrics (MAE and RMSE) for PHQ-9 score estimation. Table 3 presents the quantitative results on the validation set, including per-class breakdown for all five severity categories. A comparison between the true and predicted severity class distributions is shown in Fig. 4, confirming the close alignment between the predicted and actual class frequencies. The distribution of absolute prediction errors for PHQ-9 score estimation is shown in Fig. 5. The scatter plot of the predicted versus ground-truth PHQ-9 scores is shown in Fig. 6, demonstrating strong linear alignment. The error distribution (predicted minus true), which is approximately normally distributed and centered near zero, is shown in Fig. 7, confirming low systematic bias. The confusion matrix (Fig. 8) demonstrates near-perfect classification performance across all five severity classes. For the Minimal class, 126 out of 127 samples were correctly classified (one misclassified as Mild). For the Mild, Moderate, Moderately Severe, and Severe classes, all the samples were correctly classified (129, 129, 129, and 133 samples, respectively), yielding perfect per-class accuracy. This finding is consistent with the reported per-class precision and recall values and confirms that the model has effectively learned discriminative representations for all severity levels.

Table 3: Performance evaluation on the PHQ-TextSet validation set.

Fig. 3: Training vs validation loss.

Fig. 4: Comparison of true and predicted depression severity class distributions.

Fig. 5: Distribution of absolute prediction error for PHQ-9 score estimation.

Fig. 6: Predicted versus ground-truth PHQ-9 scores.

Fig. 7: Distribution of PHQ-9 prediction errors (Predicted − True).

Fig. 8: Confusion matrix for depression severity classification.

Table 4 presents a systematic comparison of the proposed system with representative existing approaches for depression detection and PHQ-9 assessment, evaluating differences across input types, learning strategies, model architectures, output formats, and evaluation metrics. To evaluate the effectiveness of the proposed multi-task DeBERTa model, we compared its performance with several baseline models commonly used in text classification tasks.

The following models were implemented for comparison:

• Logistic Regression with TF-IDF features[5]
• Support Vector Machine (SVM)[4]
• BERT-base (single-task classification)[11]

The proposed system outperforms both compared approaches in terms of output comprehensiveness and deployment readiness. Compared with Alves et al.,[8] the proposed approach operates on free-form user text rather than structured clinical notes, includes multi-task learning for simultaneous score and severity prediction, and achieves a higher discriminative AUC of 0.96 versus 0.81. Unlike Trotzek et al. [18], the proposed system provides clinically calibrated PHQ-9 score estimation rather than binary classification and supports confidence-aware outputs for responsible deployment.

Table 4: Comparative analysis with existing approaches.

The experimental results confirm that the proposed multi-task DeBERTa architecture effectively learns shared contextual and emotional representations from free-form text, enabling robust simultaneous prediction of PHQ-9 scores and depression severity. The overall classification accuracy of 99.85% and macro F1 score of 0.9984 across the five severity classes demonstrate that the model generalizes well to unseen data without significant class bias. Per-class analysis revealed perfect classification (F1 = 1.000) for the moderate, moderately severe, and severe categories, whereas the minimal and mild classes achieved F1 scores of 0.9960 and 0.9961, respectively, reflecting the expected ambiguity near the boundary thresholds. The MAE of 1.2495 for the PHQ-9 regression indicates that, on average, the predicted score deviates by fewer than two points from the clinical ground truth, which is within the acceptable margin for preliminary screening purposes. Three severity classes (moderate, moderately severe, and severe) achieved perfect precision, recall, and F1 scores of 1.000, whereas the minimal and mild classes had near-perfect F1 scores of 0.9960 and 0.9961, respectively. The smooth convergence of both training and validation losses across 8 epochs, with a small and consistent gap between them, indicates that the model avoids overfitting despite fine-tuning a large pretrained transformer. The use of AdamW with weight decay and warmup scheduling contributes to training stability. The disentangled attention mechanism of DeBERTa-V3 appears particularly well suited for mental health text, where the relationship between emotionally laden content words and their positional context is critical for accurate severity assessment. Confusion matrix analysis reveals that the majority of misclassifications occur between adjacent severity classes (e.g., minimal–mild and moderate–moderate–severe boundaries), which is clinically expected given the continuous and overlapping nature of the PHQ-9 score distributions near threshold boundaries. Severe cross-class misclassifications (e.g., Minimally predicted as Severe) are rare, indicating that the model captures global severity patterns effectively. While the model achieves very high classification accuracy (99.85%) and F1 score (0.9984), this performance should be interpreted in the context of the dataset characteristics. The PHQ-TextSet dataset is synthetically constructed, and the PHQ-9 is aligned, resulting in well-structured and semantically clear samples. Unlike real-world user-generated text, which is often noisy, ambiguous, and linguistically diverse, the controlled nature of the dataset reduces variability and makes classification boundaries more distinct. Consequently, the reported performance represents an upper bound estimate under controlled conditions. Future work will focus on evaluating the model on real-world datasets (e.g., social media text) to assess its generalizability and robustness.

Several limitations should be acknowledged. The PHQ-TextSet dataset, while purpose-built for PHQ-9 alignment and constructed by the authors, may not fully capture the linguistic diversity of real-world user populations across different demographics, languages, and cultural contexts. The system is intended solely as a supportive screening tool and not as a clinical diagnostic instrument. Ethical considerations are critical in the deployment of automated mental health screening systems. The proposed model is intended solely as a preliminary screening tool and not as a substitute for professional diagnosis. Incorrect predictions may lead to false reassurance or unnecessary concern, highlighting the importance of human oversight. Additionally, the use of text-based data raises privacy concerns, particularly when deployed in real-world applications involving personal user inputs. Appropriate data handling, anonymization, and user consent mechanisms must be implemented. Bias is another potential limitation, as the synthetic dataset may not fully represent diverse linguistic, cultural, and demographic variations. Future work will focus on incorporating real-world datasets and conducting fairness evaluations to ensure equitable performance across different populations. Future work will explore dataset expansion with diverse real-world samples, clinical validation with mental health professionals, multimodal input integration, and enhanced model interpretability through attention visualization and explanation methods.

In this study, a multi-task DeBERTa-V3-based depression detection system that jointly predicts PHQ-9 scores and depression severity levels from user-entered free-form text is presented. The proposed architecture integrates a pretrained transformer encoder with separate regression and classification heads trained jointly using a combined MSE and cross-entropy loss function. The system was trained and evaluated on the PHQ-TextSet dataset, an original author-constructed dataset comprising 3,235 annotated text samples across five PHQ-9 severity classes. The model achieved a classification accuracy of 99.85%, a macro F1 score of 0.9984, and a mean absolute error of 1.2495 for the PHQ-9 score regression on the validation set. The weighted AUC of 0.96 further demonstrates strong discriminative performance across severity classes. These results confirm that transformer-based multitask learning can effectively bridge the gap between clinical PHQ-9 assessment frameworks and everyday natural language expression, offering a scalable and accessible alternative to structured questionnaire-based screening. The deployment of the trained model as a real-time FastAPI service highlights the practical viability of the proposed system for integration into digital mental health platforms, telemedicine systems and educational wellness applications. The confidence score output supports transparent and responsible clinical usage. Future research directions include expanding the training dataset with diverse real-world samples, incorporating multimodal inputs, conducting clinical validation studies, and improving model interpretability for responsible AI deployment in mental healthcare.

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