Depression

Leveraging Machine Learning to Uncover Ethnic-Specific Predictors of Maternal Postpartum Depression.

Maternal postpartum depression (PPD) is a significant mental health issue that affects over 15% of mothers, with adverse effects on their children. Ethnic disparities in PPD represent a pressing public health concern, and minority mothers are found to have a higher likelihood of being affected by PPD. Identifying specific risk factors for these mothers is crucial for developing targeted preventive...

Identification and experimental confirmation of TBC1D10C as a shared transcriptional link between abdominal aortic aneurysm and major depressive disorder.

BACKGROUND: Abdominal aortic aneurysm (AAA) and major depressive disorder (MDD) are prevalent conditions with substantial global health burdens. Growing clinical evidence indicates a close relationship between them, implicating shared pathogenic mechanisms of immune dysregulation and inflammation. Deciphering this molecular interplay is critical, as it may reveal unified therapeutic strategies for...

Prevalence and factors associated with suicidal ideation based on the Malaysia National Health and Morbidity Survey: A machine learning analysis.

BACKGROUND: This study estimated the prevalence of past-two-week suicidal ideation and examined its demographic, clinical, and health-related correlat...

Indirect, Machine Learning-Based Suicide Risk Screening: Evidence from Cross-National Validation.

Dexpression recognition from EEG based on nonlinear analysis and adaptive feature fusion.

Machine learning techniques have recently shown significant promise in electroencephalograph (EEG)-based depression recognition. However, existing met...

Predicting the need for electroconvulsive therapy via machine learning trained on electronic health record data.

OBJECTIVES: Electroconvulsive therapy (ECT) is an effective treatment of severe manifestations of mental illness. Since delay in initiation of ECT can...

Accelerated brain aging in prolonged grief disorder of later life: Influence of comorbid depression.

BACKGROUND: Prolonged Grief Disorder (PGD) in later life may involve volumetric patterns indicative of accelerated brain aging. This study examined wh...

Integrated AI-Driven Triage and Referral for Newcomer Mental Health in Ontario: Addressing Anxiety and Depression.

Systemic barriers, including language, navigation complexity, and long specialist wait-times, result in the under-utilization of mental health service...

Using AI to Train Future Clinicians in Depression Assessment: Feasibility Study.

BACKGROUND: Depression is a major global health care challenge, causing significant individual distress but also contributing to a substantial global ...

Evaluating an AI-Enabled Mobile Mental Health Monitoring Tool Among Family Caregivers of Adults Living With Cancer: Single-Arm Feasibility and Acceptability Trial Protocol.

BACKGROUND: Psychological distress, particularly symptoms of depression and anxiety (D&A), is highly prevalent among family caregivers of individuals ...

Feasibility of machine learning classification of depression and anxiety symptoms among college students using 3D gait and sit-to-walk biomechanics.

BACKGROUND: Depression and anxiety are common and burdensome. Objective movement assessment may complement questionnaires by providing low burden scre...

Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods.

The complexity of traditional Chinese medicine (TCM), with its numerous components, targets, and varying efficacy, presents challenges for current eva...

Facing the Silent Crisis: Suicidal Ideation and Suicide in the Nursing Profession.

BACKGROUND: This manuscript explores the silent yet urgent crisis of suicidal ideation and suicide within the nursing profession. Despite being one of...

Text-Based Depression Estimation Using Machine Learning With Standard Labels: Systematic Review and Meta-Analysis.

BACKGROUND: Depression affects people's daily lives and even leads to suicidal behavior. Text-based depression estimation using natural language proce...

Immune-related biomarkers for major depressive disorder identified via integrated bioinformatics and machine learning.

Background: Major depressive disorder (MDD) is a leading cause of disability worldwide, yet its early and objective diagnosis remains challenging due ...

Multimodal machine learning models for predicting remission in major depressive disorder using clinical data, blood biomarkers, and DNA methylation.

Major depressive disorder (MDD) is a leading global health burden, yet only one-third of patients achieve remission with initial antidepressant therap...

Functional Inputs to the Subgenual Cingulate Cortex Differentiate Levels of Depressive Symptom Severity in Neurotypical Young Adults: Granger Causality and Clustering Analyses.

Extant research has implicated functional connectivity of the subgenual anterior cingulate cortex (sgACC) in major depressive disorders or depressive ...

Predicting suicidal and self-harm ideation using ecological momentary assessment: deep learning analysis in a general population sample.

MS-STFNN: A multi-scale spatio-temporal fusion neural network for fMRI-based depression diagnosis.

Major depressive disorder (MDD) is a common mental disorder. Current clinical diagnosis primarily relies on subjective scales, underscoring an urgent ...

Assessment of mental and behavioural non-motor symptoms of Parkinson's Disease using Artificial Intelligence (AI): a systematic review.

BACKGROUND: Parkinson's disease is a progressive neurodegenerative disorder with both motor and non-motor symptoms. Mental and behavioural non-motor s...