Psychiatry

Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder with limited objective diagnostic tools, highlighting the urgent need for objective, biology-based diagnostic frameworks in precision psychiatry. We integrate urinary metabolomics with an interpretable machine learning framework to identify biochemical signatures associated with ADHD. Targeted metabolomic pr...

Early Linguistic Pattern of Anxiety from Social Media Using Interpretable Linguistic Features: A Multi-Faceted Validation Study with Author-Disjoint Evaluation

Anxiety affects hundreds of millions of individuals globally, yet large-scale screening remains limited. Social media language provides an opportunity for scalable detection, but current models often lack interpretability, keyword-robustness validation, and rigorous user-level data integrity. This work presents a transparent approach to social media-based anxiety detection through linguistically i...

Suicide- and crisis-risk detection using large language models in mental-health chatbots

ObjectiveLarge language models (LLMs) are increasingly embedded in mental-health chatbots, yet safe deployment is limited by two unresolved challenges...

Leveraging Pretrained Vision Transformers for classifying Alcohol Use Disorder using Raw Resting-State EEG

Alcohol Use Disorder (AUD) is a prevalent and debilitating neuropsychiatric condition characterized by compulsive alcohol consumption, impaired contro...

Biomechanical analysis on neurotypical and autism spectrum disorder people during human-cobot interaction.

Biomechanical analysis is essential for assessing subjects interacting with robotic setups and platforms. However, in industrial scenarios, workers' b...

Decoding chronic stress: From behavioral-molecular dynamics in mice to clinical implications of cortisol and IL-17 in depression severity.

BACKGROUNDS: The etiology of depression involves chronic stress, a recognized determinant of onset and severity. This study adopts a translational app...

Machine learning-based predictive modeling of depressive symptoms in Chinese adolescents.

BACKGROUND: The aim is to develop prediction models by lifestyles indicators as well as socioeconomic status to predict the risk of depressive symptom...

Call category classification of the isolation-induced ultrasonic vocalizations emitted by BTBR TItpr3/J mouse pups exposed to different perinatal diets.

Autism spectrum disorder (ASD) is characterized by deficits in social communication and repetitive behaviors/restricted interests that may be diagnose...

Abnormalities of brain dynamics based on large-scale cortical network modeling in autism spectrum disorder.

Synaptic increase is a common phenomenon in the brain of autism spectrum disorder (ASD). However, the impact of increased synapses on the neurophysiol...

RIPTOSO: The development of a screening tool for adverse events during forensic-psychiatric inpatient treatments of offenders with schizophrenia spectrum disorders.

Adverse events such as compulsory measures, absconding, illicit substance use, self-harm, aggressive behavior, and prolonged hospitalization pose sign...

Investigating the interpretability of ChatGPT in mental health counseling: An analysis of artificial intelligence generated content differentiation.

The global impact of COVID-19 has caused a significant rise in the demand for psychological counseling services, creating pressure on existing mental ...

Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data.

Accurate prediction of depressive symptoms in healthy individuals can enable early intervention and reduce both individual and societal costs. This st...

Prediction of remission of pharmacologically treated psychotic depression: A machine learning approach.

BACKGROUND: The combination of antidepressant and antipsychotic medication is an effective treatment for major depressive disorder with psychotic feat...

Machine learning-driven risk prediction and feature identification for major depressive disorder and its progression: an exploratory study based on five years of longitudinal data from the US national health survey.

BACKGROUND: Major depressive disorder (MDD) presents significant public health challenges due to its increasing prevalence and complex risk factors. T...

Emotion Recognition in Older Adults with Quantum Machine Learning and Wearable Sensors

We investigate the feasibility of inferring emotional states exclusively from physiological signals, thereby presenting a privacy-preserving alterna...

Beyond Scale: Small Language Models are Comparable to GPT-4 in Mental Health Understanding

The emergence of Small Language Models (SLMs) as privacy-preserving alternatives for sensitive applications raises a fundamental question about thei...

Development and Evaluation of HopeBot: an LLM-based chatbot for structured and interactive PHQ-9 depression screening

Static tools like the Patient Health Questionnaire-9 (PHQ-9) effectively screen depression but lack interactivity and adaptability. We developed Hop...

Modeling the Determinants of Subjective Well-Being in Schizophrenia.

BACKGROUND: The ultimate goal of successful schizophrenia treatment is not just to alleviate psychotic symptoms, but also to reduce distress and achie...

Prediction of Adolescent Suicide Attempt by Integrating Clinical, Neurocognitive and Geocoded Neighborhood Environment Data.

BACKGROUND AND HYPOTHESIS: Suicide attempt is a complex behavior influenced by a combination of factors including clinical, neurocognitive, and enviro...

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery

In this paper, we investigate a practical yet challenging task: On-the-fly Category Discovery (OCD). This task focuses on the online identification ...