Depression

Leveraging Large Language Models for Cost-Effective, Multilingual Depression Detection and Severity Assessment

Depression is a prevalent mental health disorder that is difficult to detect early due to subjective symptom assessments. Recent advancements in large language models have offered efficient and cost-effective approaches for this objective. In this study, we evaluated the performance of four LLMs in depression detection using clinical interview data. We selected the best performing model and furt...

MedGNN: Capturing the Links Between Urban Characteristics and Medical Prescriptions

Understanding how urban socio-demographic and environmental factors relate with health is essential for public health and urban planning. However, traditional statistical methods struggle with nonlinear effects, while machine learning models often fail to capture geographical (nearby areas being more similar) and topological (unequal connectivity between places) effects in an interpretable way. ...

Leveraging Embedding Techniques in Multimodal Machine Learning for Mental Illness Assessment

The increasing global prevalence of mental disorders, such as depression and PTSD, requires objective and scalable diagnostic tools. Traditional cli...

What could be? Depends on who you ask: Using latent profile analysis and natural language processing to identify the different types and content of utopian visions.

When people think of a utopian future, what do they imagine? We examined (a) whether people's self-generated utopias differ by how much they criticize...

Datasets for Depression Modeling in Social Media: An Overview

Depression is the most common mental health disorder, and its prevalence increased during the COVID-19 pandemic. As one of the most extensively rese...

Enhancing Depression Detection via Question-wise Modality Fusion

Depression is a highly prevalent and disabling condition that incurs substantial personal and societal costs. Current depression diagnosis involves ...

Peer Disambiguation in Self-Reported Surveys using Graph Attention Networks

Studying peer relationships is crucial in solving complex challenges underserved communities face and designing interventions. The effectiveness of ...

A Systematic Review of EEG-based Machine Intelligence Algorithms for Depression Diagnosis, and Monitoring

Depression disorder is a serious health condition that has affected the lives of millions of people around the world. Diagnosis of depression is a c...

AI-Based Screening for Depression and Social Anxiety Through Eye Tracking: An Exploratory Study

Well-being is a dynamic construct that evolves over time and fluctuates within individuals, presenting challenges for accurate quantification. Reduc...

A Foundation Model for Patient Behavior Monitoring and Suicide Detection

Foundation models (FMs) have achieved remarkable success across various domains, yet their adoption in healthcare remains limited. While significant...

Generating Medically-Informed Explanations for Depression Detection using LLMs

Early detection of depression from social media data offers a valuable opportunity for timely intervention. However, this task poses significant cha...

Optimizing Large Language Models for Detecting Symptoms of Comorbid Depression or Anxiety in Chronic Diseases: Insights from Patient Messages

Patients with diabetes are at increased risk of comorbid depression or anxiety, complicating their management. This study evaluated the performance ...

Predicting Treatment Response in Body Dysmorphic Disorder with Interpretable Machine Learning

Body Dysmorphic Disorder (BDD) is a highly prevalent and frequently underdiagnosed condition characterized by persistent, intrusive preoccupations w...

Accuracy of artificial intelligence-based simulation for assessing lung vessels and volume using unenhanced computed tomography.

OBJECTIVES: The advantages of preoperative three-dimensional (3D) image simulations, which require enhanced computed tomography (ECT), for anatomical ...

Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation

Depression is a widespread mental health disorder, and clinical interviews are the gold standard for assessment. However, their reliance on scarce p...

Mixture of Experts for Recognizing Depression from Interview and Reading Tasks

Depression is a mental disorder and can cause a variety of symptoms, including psychological, physical, and social. Speech has been proved an object...

Evidence-Driven Marker Extraction for Social Media Suicide Risk Detection

Early detection of suicide risk from social media text is crucial for timely intervention. While Large Language Models (LLMs) offer promising capabi...

Eeyore: Realistic Depression Simulation via Supervised and Preference Optimization

Large Language Models (LLMs) have been previously explored for mental healthcare training and therapy client simulation, but they still fall short i...

MHQA: A Diverse, Knowledge Intensive Mental Health Question Answering Challenge for Language Models

Mental health remains a challenging problem all over the world, with issues like depression, anxiety becoming increasingly common. Large Language Mo...

Predicting Depression in Screening Interviews from Interactive Multi-Theme Collaboration

Automatic depression detection provides cues for early clinical intervention by clinicians. Clinical interviews for depression detection involve dia...