| Journal of Smart Sensors and Computing
Received: 13 February 2026; Revised: 17 March 2026; Accepted: 29 March 2026; Published Online: 31 March 2026.
J. Smart Sens. Comput., 2026, 2(1), 26205 | Volume 2 Issue 1 (March 2026) | DOI: https://doi.org/10.64189/ssc.26205
© The Author(s) 2026
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
PHQ-9 Based Depression Detection Using Text with
Multi-Task DeBERTa Model
Rahul Dagade,
1,*
Saee Darwatkar,
1
Payal Kalekar,
1
Aditya Kamble,
1
Prasad Hargude,
1
Nisha Godha,
2
and
Ganesh Jadhav
3
1
Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037, India
2
Department of Electrical Engineering, Sinhgad Institutes, Pune, Maharashtra, 411046, India
3
Department of Information Technology, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037, India
*Email: rahul.dagade@vit.edu (Rahul Dagade)
Abstract
Depression is a widespread mental health disorder that adversely affects emotional well-being, cognitive
functioning, and overall quality of life. Early and accurate detection is essential for timely intervention; however,
traditional screening methods such as the Patient Health Questionnaire-9 (PHQ-9) are often limited by
accessibility and resource constraints. To address these challenges, this study proposes a text-based automated
depression screening system that predicts both PHQ-9 scores and depression severity levels from user-
generated free-form text. The proposed approach uses DeBERTa-V3, a state-of-the-art transformer model,
within a multi-task learning framework that simultaneously performs regression and multi-class classification.
The model was trained on the PHQ-TextSet dataset, a synthetically constructed and PHQ-9-aligned corpus
comprising 3,235 annotated samples across five severity categories. By leveraging disentangled attention and
shared contextual representations, the system effectively captures nuanced linguistic and emotional patterns.
The experimental results demonstrate high performance, achieving 99.85% classification accuracy, a macro F1
score of 0.9984, a weighted AUC of 0.96, and a mean absolute error of 1.2495 for PHQ-9 score prediction.
However, these results should be interpreted as upper-bound estimates because of the structured and
controlled nature of the dataset, which differs from real-world, noisy text inputs. The model is deployed as a
real-time inference system using FastAPI, highlighting its practical applicability in digital mental health
platforms, telemedicine systems, and educational wellness tools. This work presents a scalable and accessible
alternative to traditional screening methods while maintaining alignment with clinical assessment standards.
Future work will focus on evaluating the model on real-world datasets, enhancing robustness, incorporating
multimodal data, and improving interpretability to support responsible AI deployment in mental healthcare.
Keywords: Depression detection, PHQ-9 score prediction, DeBERTa, Multi-task learning, Natural language processing,
Text-based analysis, Mental health screening.
1. Introduction
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.
2. Related work
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. Proposed architecture
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:
=
(1×768)
, (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:
(2)
(5×768)
. The predicted severity label is the argmax of this distribution. The joint
training objective minimizes the combined multi-task loss:
(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:
(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. Learning Algorithm and Mathematical Formulation
4.1 Algorithm
Algorithm 1 summarizes the complete inference procedure of the proposed multi-task depression detection
system:
Algorithm 1: Multi-Task Depression Detection with DeBERTa
Input: T (free-form text entered by the user)
Output: PHQ-9 score, severity label, confidence score
1. Tokenize the input text T using the SentencePiece tokenizer
2. Apply padding/truncation to a fixed maximum length (256 tokens)
3. Pass the tokenized sequence through the DeBERTa-V3-base encoder
4. Extract [CLS] token representation
5.
(regression head)
6.
(classification head)
7.
8.
9. Denormalize:
10. Return
4.2 Mathematical formulation
4.2.1 Input Representation
Let
(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:
(6)
4.2.2 Contextual Encoding
(7)
The DeBERTa encoder maps X to a sequence of contextual embeddings:
(8)
where H₀ ∈ ℝ
768
is the [CLS] representation used as the global semantic feature vector for both downstream
tasks.
4.2.3 PHQ-9 score normalization and regression
(9)
The PHQ-9 score is normalized to the interval [0,1] as follows: y_norm =
The regression head predicts the
following:
(10)
The regression loss is the mean squared error:
(11)
4.2.4 Severity Classification
(12)
The classification head produces a probability distribution over severity classes:
The predicted severity class is obtained as follows:
(13)
The classification loss is computed using cross-entropy:
(14)
4.2.5 Combined multi-task loss
(15)
The overall training objective combines regression and classification losses:
(16)
where λ = 1.0. The model parameters are updated using AdamW optimization with weight decay to minimize
L
total 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:
(17)
(18)
(19)
(20)
The regression performance is evaluated using the mean absolute error (MAE) and root mean square error
(RMSE). The MAE is defined as follows:
(21)
Root Mean Squared Error:
(22)
Discriminative ability was assessed using the ROC-AUC curve. The confidence score is computed as max(
)
from the softmax output.
5. Experimental results
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.
Attribute
Value
Dataset Name
PHQ-TextSet (PHQ-9 Text Assessment Dataset)
Dataset Type
PHQ-9-aligned free-form text dataset (author-constructed)
Total Samples
3,235 (after removing 3 incomplete entries)
PHQ-9 Score Range
0 – 27
Severity Classes
5 (Minimal, Mild, Moderate, Moderately Severe, Severe)
Minimal (0–4)
635 samples (19.6%)
Mild (5–9)
644 samples (19.9%)
Moderate (10–14)
645 samples (19.9%)
Moderately Severe (15–19)
643 samples (19.9%)
Severe (20–27)
668 samples (20.6%)
Training/Validation Split
80%/20% (stratified)
Training Samples
2,588
Validation Samples
647
Maximum Token Length
256 subword tokens (SentencePiece)
Text Encoder
DeBERTa-V3-base (Hugging Face Transformers)
Input Type
User-entered free-form natural language text
Learning Strategy
Multi-task (Regression + Classification)
Table 2: Model hyperparameter configuration.
Hyperparameter
Value
Pretrained Model
microsoft/deberta-v3-base
Max Sequence Length
256 tokens
Optimizer
AdamW
Learning Rate
2×10⁻⁵
Weight Decay
0.01
Batch Size
8
Epochs
8
Warmup Ratio
0.1
MTL Loss Weight (λ)
1.0 (equal task weighting)
Dropout Rate
0.1 (applied to task heads)
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.
Metric
Task
Value
Accuracy
Classification
99.85%
Macro Precision
Classification
0.9985
Macro Recall
Classification
0.9984
Macro F1-Score
Classification
0.9984
Mean Absolute Error (MAE)
Regression (PHQ-9)
1.2495
Root Mean Squared Error (RMSE)
Regression (PHQ-9)
2.31
Weighted AUC (ROC)
Classification
0.96
Per-Class Breakdown (Classification)
Minimal — P: 1.000 / R: 0.9921 / F1: 0.9960
Classification
Support: 127
Mild — P: 0.9923 / R: 1.000 / F1: 0.9961
Classification
Support: 129
Moderate — P: 1.000 / R: 1.000 / F1: 1.000
Classification
Support: 129
Moderately Severe — P: 1.000 / R: 1.000 / F1: 1.000
Classification
Support: 129
Severe — P: 1.000 / R: 1.000 / F1: 1.000
Classification
Support: 133
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.
6. Comparative analysis with existing approaches
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.
Aspect
Ref. [8]
Ref. [18]
Proposed system
Objective
PHQ-9 severity from clinical
text
Early depression detection
from text
PHQ-9 score + severity from free text
Input Type
Structured clinical notes
Social media text
Free-form user text
Model
Traditional ML/NLP
CNN + linguistic metadata
DeBERTa-V3 transformer
Learning
Strategy
Single-task
Single-task
Multitask (Regression + Classification)
Output
PHQ-9 score only
Binary (depressed/not)
PHQ-9 score + severity + confidence
Key Metric
AUC ≈ 0.81
F1 ≈ 0.72
Accuracy 99.85%, Macro F1 0.9984, MAE
1.2495, AUC 0.96
Deployment
Not reported
Not reported
FastAPI REST API
Dataset
Structured EHR notes
ReachOut social media
PHQ-TextSet (3,235 samples)
7. Discussion
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.
8. Conclusion
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.
CRediT Author Contribution Statement
Rahul Dagade: Conceptualization; Methodology; Formal analysis; Investigation; Validation; Supervision;
Project administration; Writing - Review & editing. Saee Darwatkar: Formal analysis; Investigation. Payal
Kalekar: Data curation; Resources; Software. Aditya Kamble: Data curation; Resources; Software; Writing -
Review & editing. Prasad Hargude: Data curation; Resources; Software; Writing - Review & editing. Nisha
Godha: Conceptualization; Investigation; Writing - Review & editing. Ganesh Jadhav: Data curation; Formal
analysis; Investigation; Writing - Review & editing. All the authors were supervised and supported by their
advisors from the corresponding institution. All authors have read and agreed to the published version of the
manuscript.
Funding Declaration
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors.
Data Availability Statement
The PHQ-TextSet dataset used in this study was constructed by the Authors and is available upon reasonable
request to the corresponding author.
Conflict of Interest
There is no conflict of interest.
Artificial Intelligence (AI) Use Disclosure
The authors confirm that no artificial intelligence (AI)-assisted technologies were used in the writing of the
manuscript, and no images were generated or manipulated using AI. AI-based tools were used solely for
language editing to improve grammar, clarity, and readability, in accordance with journal policy. The authors
take full responsibility for the accuracy, originality, and integrity of the work.
Supporting Information
Not applicable.
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