Depression

Machine learning classification of patients after suicide attempts using demographic data, EEG connectivity and heart rate variability.

OBJECTIVE: The aim of this study was to develop a way to distinguish suicidal patients based on their electrophysiologic (EEG connectivity and heart rate variability) and demographic data. Various machine learning algorithms were compared to find the best models and features for this task. METHODS: Using a dataset of 140 subjects (or 87 subjects for HR models) from previous studies, different mach...

Attentional Avoidance of Sexual Cues in Psychogenic Erectile Dysfunction: An Eye-Tracking and Machine Learning Study.

BACKGROUND: Psychogenic erectile dysfunction (pED) is a prevalent male erectile dysfunction without organic causes, and difficulties in erection attainment and post-penetration maintenance often co-occur. Although neuroimaging studies have implicated abnormalities in attentional control networks, direct behavioral evidence of how pED patients with this comorbid pattern process sexual cues is lacki...

Evaluating the role of cost and fairness in machine learning models of suicide risk.

Increasing value in the Veterans Affairs Healthcare System (VA) with precision health: a continuing landmark collaboration with the Department of Energy.

OBJECTIVE: Phase II of MVP-CHAMPION, a federal collaboration between the Veterans Affairs Healthcare System (VA) and the Department of Energy (DoE), l...

Intelligent virtual agents in psychotherapy: a safety evaluation across high-risk mental health scenarios.

The growing burden of mental illness and limited access to evidence-based psychotherapy have increased interest in artificial intelligence (AI)-driven...

Systematic review of machine learning and deep learning models for EEG-based detection of depression.

OBJECTIVE: Depression is a leading cause of global disability, motivating the development of objective and scalable diagnostic approaches. Quantitativ...

Integrative multi-kingdom gut microbiome analysis uncovers clinical signatures of major depressive disorder.

BACKGROUND: Accumulating evidence indicates that gut microbiome is significantly altered in major depressive disorder (MDD). However, most studies hav...

A relational agent for treating substance use in adults: A randomized controlled trial with a psychoeducational comparator.

BACKGROUND: Highly accessible and scalable, digital mental health interventions can reduce barriers associated with traditional treatment. Woebot for ...

Comparing ChatGPT-4o and Gemini 1.5 Pro in Adolescent Psychiatric Emergencies: A Real-World Evaluation of AI Support in Suicide Risk Assessment.

ObjectiveThis study aimed to evaluate the performance of large language models-ChatGPT-4o and Gemini 1.5 Pro-in assessing suicide risk and guiding tre...

Graph theory reveals functional connectome disruptions in adolescent major depressive disorder with childhood trauma.

BACKGROUND: Childhood trauma (CT) is a major risk factor for adolescent major depressive disorder (MDD), yet its neurobiological underpinnings and lon...

Heterogeneity of depression and suicidal ideation among college students in Eastern China: a latent profile analysis and machine learning approach.

To investigate the heterogeneity of depression and suicidal ideation among Chinese college students and to examine the roles of family and individual ...

Boon or a bane: Different usage of LLM correlates with mental health.

BACKGROUND: While large language models (LLMs) are increasingly integrated into daily life, the relationship between purpose-specific usage and mental...

Comprehensive integrative analysis of Exosome-associated genes in major depressive disorder: Diagnostic biomarker discovery, immune modulation, and therapeutic implications.

OBJECTIVE: This study systematically analyzed the expression profiles of exosome-associated genes in Major Depressive Disorder (MDD), constructed diag...

Cerebellar gray matter volume difference in first-episode bipolar and unipolar depression.

BACKGROUND: Early differential diagnosis of bipolar disorder (BD) and unipolar depression (UD) remains a major clinical challenge, especially during t...

Effectiveness of a Fully Automated Mobile Therapeutic Versus a General Chatbot in Reducing Depression and Anxiety and Improving Well-Being: Feasibility Randomized Controlled Trial.

BACKGROUND: Given the increasing prevalence of depression and anxiety disorders and enduring barriers to care, there is a critical need for alternativ...

Multimodal brain imaging-based classification of functional constipation subtypes using machine learning.

Functional constipation (FC) is a common gastrointestinal condition often accompanied by anxiety and depression status (FCAD). Gastrointestinal sympto...

Impaired regional structure-function coupling as novel neurophenotype: mechanistic insights and diagnostic exploration in treatment-resistant depression.

Treatment‑resistant depression (TRD) is one of the toughest clinical challenges in psychiatry, characterized by high recurrence, heavy disease burden,...

Predicting student mental health through entropy-based features and interpretable cross-attention transformer networks.

Mental health is becoming a major concern for students in today's fast-changing world. Mental health challenges have impact on every aspect of life in...

The effect of robot-based interventions on depression in older adults with cognitive impairment: A systematic review and meta-analysis.

OBJECTIVE: To assess the effectiveness of robot-based interventions in improving depressive symptoms among older adults with cognitive impairment, and...

Popular Online Content as a Treatment-as-Usual Control in Digital Mental Health Intervention Trials: Secondary Analysis of Two Online Randomized Controlled Trials With Repeated Measures.

BACKGROUND: Treatment-as-usual (TAU) conditions are intended to reflect the support typically received in routine treatment settings. For digital ment...