Psychiatry

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 in authentically capturing diverse client traits and psychological conditions. We introduce \textbf{Eeyore}, an 8B model optimized for realistic depression simulation through a structured alignment framework, incorporating expert input at every stage....

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 Models (LLMs) have seen a vast application in healthcare, specifically in answering medical questions. However, there is a lack of standard benchmarking datasets for question answering (QA) in mental health. Our work presents a novel multiple choice da...

Unsupervised Clustering Approaches for Autism Screening: Achieving 95.31% Accuracy with a Gaussian Mixture Model

Autism spectrum disorder (ASD) remains a challenging condition to diagnose effectively and promptly, despite global efforts in public health, clinic...

Discovering the influence of personal features in psychological processes using Artificial Intelligence techniques: the case of COVID19 lockdown in Spain

At the end of 2019, an outbreak of a novel coronavirus was reported in China, leading to the COVID-19 pandemic. In Spain, the first cases were detec...

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...

A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions

Mental health remains a critical global challenge, with increasing demand for accessible, effective interventions. Large language models (LLMs) offe...

Measuring Anxiety Levels with Head Motion Patterns in Severe Depression Population

Depression and anxiety are prevalent mental health disorders that frequently cooccur, with anxiety significantly influencing both the manifestation ...

Representation Learning to Advance Multi-institutional Studies with Electronic Health Record Data

The adoption of EHRs has expanded opportunities to leverage data-driven algorithms in clinical care and research. A major bottleneck in effectively ...

Normative Cerebral Perfusion Across the Lifespan

Cerebral perfusion plays a crucial role in maintaining brain function and is tightly coupled with neuronal activity. While previous studies have exa...

Time2Lang: Bridging Time-Series Foundation Models and Large Language Models for Health Sensing Beyond Prompting

Large language models (LLMs) show promise for health applications when combined with behavioral sensing data. Traditional approaches convert sensor ...

[Prediction of depression symptoms in seniors and analysis of influencing factors based on explainable machine learning].

This study aims to construct a machine learning model to predict depression symptoms in the elderly and analyze the key influencing factors of depres...

HamRaz: A Culture-Based Persian Conversation Dataset for Person-Centered Therapy Using LLM Agents

This paper presents HamRaz, a novel Persian-language mental health dataset designed for Person-Centered Therapy (PCT) using Large Language Models (L...

Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models

Depression is one of the leading causes of disability worldwide, posing a severe burden on individuals, healthcare systems, and society at large. Re...

Multimodal Cognitive Reframing Therapy via Multi-hop Psychotherapeutic Reasoning

Previous research has revealed the potential of large language models (LLMs) to support cognitive reframing therapy; however, their focus was primar...

Multi-Site rs-fMRI Domain Alignment for Autism Spectrum Disorder Auxiliary Diagnosis Based on Hyperbolic Space

Increasing the volume of training data can enable the auxiliary diagnostic algorithms for Autism Spectrum Disorder (ASD) to learn more accurate and ...

Explainable and externally validated machine learning for neuropsychiatric diagnosis via electrocardiograms

Electrocardiogram (ECG) analysis has emerged as a promising tool for identifying physiological changes associated with neuropsychiatric conditions. ...

Innovative Framework for Early Estimation of Mental Disorder Scores to Enable Timely Interventions

Individual's general well-being is greatly impacted by mental health conditions including depression and Post-Traumatic Stress Disorder (PTSD), unde...

Multimodal Data-Driven Classification of Mental Disorders: A Comprehensive Approach to Diagnosing Depression, Anxiety, and Schizophrenia

This study investigates the potential of multimodal data integration, which combines electroencephalogram (EEG) data with sociodemographic character...

DiversityOne: A Multi-Country Smartphone Sensor Dataset for Everyday Life Behavior Modeling

Understanding everyday life behavior of young adults through personal devices, e.g., smartphones and smartwatches, is key for various applications, ...

MDD-SSTNet: detecting major depressive disorder by exploring spectral-spatial-temporal information on resting-state electroencephalography data based on deep neural network.

Major depressive disorder (MDD) is a psychiatric disorder characterized by persistent lethargy that can lead to suicide in severe cases. Hence, timely...