Psychiatry

AI as the Interpreter for Identifying Root Causes and Emotional Themes in Mental Health Narratives on Reddit Using AutoML and PaLM 2: Mixed Methods Study.

BACKGROUND: Mental health issues continue to increase worldwide, often intensified by stigma and lack of awareness. Social media has become a major space where individuals articulate their emotional and psychological experiences. However, little is known about how AI models interpret these narratives, particularly when contextual reasoning and alignment with human judgment are required. OBJECTIVE:...

The Role of Artificial Intelligence in Addressing Menopausal Mental Health Challenges.

Menopause marks a crucial transition in a woman's life and is often accompanied by physical and psychological changes that can adversely affect mental health. Depression, anxiety, cognitive changes, and sleep disturbances are common during the menopausal transition, yet they are frequently underdiagnosed and undertreated, particularly in lowand middle-income countries. Emerging technologies, espec...

Predicting depression severity among people living with HIV in Oman using random forest.

Depression is the most common psychiatric comorbidity among people living with HIV and is associated with an increased risk of disease progression. Ho...

Pharmacologic and expectancy effects in depression: Associations with inter-network resting-state connectivity.

Depression involves dysregulation across large-scale neural networks, yet substantial heterogeneity in resting-state functional connectivity (rsFC) ac...

Design and optimization of deep learning model based on multimodal data fusion for dynamic mental health assessment.

The dynamic assessment of mental health has emerged as a hotspot for study and application due to the rise in social pressure. However, onventional me...

Attention-Enhanced Static-Dynamic Fusion Network for Major Depressive Disorder Diagnosis.

OBJECTIVE: Accurate depression classification using fNIRS signals is critical for objective auxiliary diagnosis, yet many existing methods separately ...

Machine learning-enabled behavioural and psychological symptoms of dementia management intervention for dementia caregivers: protocol for a hybrid factorial SMART-MRT trial.

BACKGROUND: Behavioural and psychological symptoms of dementia (BPSD) affect over 90% of people living with dementia and are a major contributor to st...

Analysis of health anxiety trajectories in rabies exposure patients based on machine learning.

OBJECTIVE: To identify the dynamic evolution trajectory of health anxiety in patients with initial rabies exposure and to conduct predictive analysis ...

Unifying the hallmarks of major depression through neuroimmune-metabolic-oxidative (NIMETOX) dysregulation: a mechanistic systems framework.

Current state-of-the-art neuroimmune, metabolic, and oxidative stress (NIMETOX) knowledge that has been developed in clinical major depressive disorde...

Prenatal environmental exposures associated with ADHD diagnosis in children: a multicentre exposome analysis.

Attention deficit hyperactivity disorder (ADHD) is one of the most diagnosed neurodevelopmental conditions. While its aetiology is primarily attribute...

Frequency-decomposed EEG microstate analysis reveals altered cross-frequency coordination in adolescent major depressive disorder.

Adolescent major depressive disorder (MDD) involves alterations in large‑scale brain network dynamics. However, conventional EEG microstate studies ty...

White Matter Perivascular Space Burden in Children With Autism Spectrum Disorder and Typically Developing Controls: An Automated Quantitative MRI Study.

BACKGROUND: Perivascular spaces (PVS) are compartments involved in brain waste clearance. PVS are commonly observed in typically developing (TD) child...

What drives generational differences in subjective well-being? A machine learning study of Chinese migrant workers in the Yangtze River Delta.

The subjective well-being of migrant workers amid China's rapid urbanization has emerged as a critical issue, intersecting social policy and mental he...

Machine learning model for the detection of autism spectrum disorder using electroretinogram signals.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disease marked by limited or repetitive behaviors as well as ongoing difficulties with social c...

Leveraging social media footprints for predicting college student anxiety: a machine learning approach.

BACKGROUND: Anxiety is one of the most prevalent mental health concerns among college students worldwide, yet traditional assessment methods relying o...

Phenotyping antidepressant treatment response with deep learning in electronic health records.

Efficient, accurate phenotyping for antidepressant treatment response in electronic health records (EHRs) could facilitate precision psychiatry applic...

Predictors of Clinically Significant Anxiety in Non-autistic Siblings of Children With Autism.

OBJECTIVE: As the prevalence of autism rises, attention to the well-being of siblings of autistic individuals is increasingly important. The present s...

Week-Ahead Prediction of High-Risk Drinking Episodes Among Young Adults Using Wearable Biosignals and Psychological Vulnerabilities: Prospective Observational Machine Learning Study.

BACKGROUND: Although machine learning has increasingly been used to predict mental health symptoms and maladaptive behaviors, real-world prediction of...

Large Language Model-Assisted Annotation Framework for Cross-Platform Analysis of Online Autism Communities: Implications for Parent Education and Digital Support.

BACKGROUND: Online health communities (OHCs) are important channels for families of children with autism spectrum disorder to obtain health informatio...

Using Machine Learning with the Brief Symptom Inventory to Screen for Comorbid Addictions: A Proof-of-Principle Study.

INTRODUCTION: Mental health disorders are leading causes of morbidity worldwide and often co-occur with substance use and other addictive behaviors. H...