Depression

A multi-metric profiling of OFC subregion connectivity identifies system-level alterations in major depressive disorder.

BACKGROUND: Orbitofrontal cortex (OFC) dysfunction contributes to major depressive disorder (MDD), particularly through disrupted network interactions. Current studies lack whole-brain coverage and fine-grained OFC mapping. We employed OFC parcellation and multi-metric connectivity to comprehensively profile OFC-related network disturbances in MDD. METHODS: OFC was parcellated into medial (MOFC), ...

Optimal 24-Hour Sleep Duration and Depression Risk in Chinese Middle-Aged and Elderly: A Machine Learning and RCS Analysis of Nonlinear Associations.

OBJECTIVES: To investigate the relationship between sleep (nocturnal sleep and total 24-hour sleep) and depressive symptoms in middle-aged and elderly people using data from the China Health and Retirement Longitudinal Study (CHARLS). STUDY DESIGN: Cross-sectional study. METHODS: A total of 13,718 middle-aged and elderly people aged 45 and above from the China Health and Retirement Longitudinal St...

Digital Markers for Passive Remote Monitoring of Bipolar Disorder: Systematic Review.

BACKGROUND: Bipolar disorder (BD) features episodic shifts among mania, hypomania, depression, mixed states, and euthymia. Timely detection of mood tr...

Exploring Applications of AI in the Crisis Line Sector: Protocol for a Scoping Review.

BACKGROUND: Crisis helplines are a vital component of a robust public health approach to suicide prevention as they are often free, accessible, and pr...

Predicting US Army Soldier Suicide-Related Behaviors at the Time of Periodic Health Assessments.

IMPORTANCE: Suicide is the leading cause of death among active-duty US Army soldiers. Evidence-based preventive interventions exist but need to be tar...

Scalable prediction of suicidal risk in university students: a three steps machine learning approach in university settings.

Suicide is a leading cause of death among young adults, with university students representing a particularly vulnerable subgroup. Although prevention ...

The use of large language models in automated depression detection.

BACKGROUND: Large language models have been evaluated on many healthcare tasks, including depression screening. However, it is unclear whether estimat...

Multidimensional thermal anomalies and suicide mortality: Acute risks, delayed effects, and the operational utility of early warning systems.

The rising frequency of extreme temperature events due to climate change presents an urgent public health challenge, particularly regarding their comp...

Artificial intelligence and suicide prevention: Promise and pitfalls.

Real-world analysis of depression-related adverse events associated with the adsorbed anthrax vaccine: integrating pharmacovigilance signals, machine learning risk prediction, and transcriptomic mechanisms.

BACKGROUND: Anthrax remains a life-threatening zoonotic disease in resource-limited settings. Adsorbed anthrax vaccine (AVA, BioThrax) is the only Uni...

Measuring Depression Severity With Clinical Global Impression-Severity Scale Scores From Clinical Notes Using Large Language Models: Validation Study.

BACKGROUND: Real-world psychiatric care is marked by wide heterogeneity in clinical presentations and outcomes, underscoring the need for systematic a...

The diagnostic value of the circadian rhythm gene KLF10 in anxiety-depressive disorders and its neuroimmune regulatory mechanisms.

BACKGROUND: Anxiety-depressive disorder (ADD) is one of the important subtypes of depression, which has been shown to be closely related to neuroimmun...

Patterns of Engagement With an AI Conversational Agent for Mental Health and Associations With Anxiety and Depression: Cross-Sectional Study.

BACKGROUND: Digital mental health interventions using conversational AI agents are increasingly being adopted as scalable alternatives to traditional ...

Deep learning reveals a neurocomputational mechanism predicting depression risk in adolescents.

Early detection and prevention of psychiatric disorders, particularly depression, remain as major global health challenges, yet reliable tools for ide...

Decoding how macronutrients influence depressive symptoms: Combined insights from regression, nonlinear modeling, and machine learning: a cross-sectional study using NHANES data.

Dietary management is an essential component of comprehensive depression care;however, studies simultaneously evaluating multiple macronutrients and t...

Prediction of Clinically Meaningful Improvement After Internet-Delivered Cognitive Behavioral Therapy for Depression and Anxiety Disorders: Machine Learning-Based Predictive Model Development and Temporal Validation Study.

BACKGROUND: Up to 50% of patients treated with internet-delivered cognitive behavioral therapy (ICBT) for depression and anxiety disorders do not expe...

Mortality Research With Artificial Intelligence Tools: Applying Language Models to the National Violent Death Reporting System Suicide Narratives, 2003-2023.

Suicide deaths in the United States have increased for a generation with no current evidence of meaningful decline. With advances in data collection a...

Machine Learning Prediction of First Suicide Attempts in Early Adolescence Among Child Ideators.

OBJECTIVE: Identifying those at highest risk of making a first suicide attempt during adolescence is crucial to inform early suicide prevention. Our s...

Machine Learning Classification of Prevalent Chronic Disease Using Multidimensional Social, Behavioral, and Psychological Determinants: A Cross-Sectional Analysis of the 2024 National Health Interview Survey.

BACKGROUND: Chronic diseases account for approximately 90% of the $4.5 trillion annual healthcare expenditure in the United States. While traditional ...

Deep learning for EEG-based depression detection: A systematic literature review of methods and interpretability techniques.

Depression is a widespread mental illness in which there is a noticeable difference in the quality of life of people. Electroencephalography (EEG) see...