Depression Detection
25 papers with code • 4 benchmarks • 5 datasets
Depression Detection is the problem of identifying signs of depression in individuals. These signs might be identified in peoples’ speech, facial expressions and in the use of language.
Benchmarks
These leaderboards are used to track progress in Depression Detection
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Datasets
SMHD
Perla Dataset
Latest papers with no code
Test-Time Training for Depression Detection
Previous works on depression detection use datasets collected in similar environments to train and test the models.
Assessing ML Classification Algorithms and NLP Techniques for Depression Detection: An Experimental Case Study
The case study is based on the Distress Analysis Interview Corpus - Wizard-of-Oz (DAIC-WOZ) dataset, which is designed to support the diagnosis of mental disorders such as depression, anxiety, and PTSD.
MOGAM: A Multimodal Object-oriented Graph Attention Model for Depression Detection
In conclusion, we believe that the proposed model, MOGAM, is an effective solution for detecting depression in social media, offering potential benefits in the early detection and treatment of this mental health condition.
Depression Detection on Social Media with Large Language Models
Depression detection aims to determine whether an individual suffers from depression by analyzing their history of posts on social media, which can significantly aid in early detection and intervention.
Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking
Depression-diagnosis-oriented chat aims to guide patients in self-expression to collect key symptoms for depression detection.
MoodCapture: Depression Detection Using In-the-Wild Smartphone Images
MoodCapture presents a novel approach that assesses depression based on images automatically captured from the front-facing camera of smartphones as people go about their daily lives.
When LLMs Meets Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large Language Models for Depression Detection
In addition, this approach is not only valuable for the detection of depression but also represents a new perspective in enhancing the ability of LLMs to comprehend and process speech signals.
Illuminate: A novel approach for depression detection with explainable analysis and proactive therapy using prompt engineering
This paper introduces a novel paradigm for depression detection and treatment using advanced Large Language Models (LLMs): Generative Pre-trained Transformer 4 (GPT-4), Llama 2 chat, and Gemini.
Data Quality Matters: Suicide Intention Detection on Social Media Posts Using a RoBERTa-CNN Model
In the meanwhile, RoBERTa-CNN outperforms competitive methods, demonstrating the robustness and ability to capture nuanced linguistic patterns for suicidal intentions.
Few-Shot Learning for Chronic Disease Management: Leveraging Large Language Models and Multi-Prompt Engineering with Medical Knowledge Injection
On the depression detection task, our method (F1 = 0. 975~0. 978) significantly outperforms traditional supervised learning paradigms, including feature engineering (F1 = 0. 760) and architecture engineering (F1 = 0. 756).
