Depression

Multimodal Domain Generalization for Depression Detection: An Attention-Based BiLSTM Network With Domain-Adversarial Training.

Automatic depression detection with deep learning has shown promise, but often suffers from limited generalization due to domain shift arising from interspeaker variability. To address this critical issue, we present the first patient-independent multimodal depression detection framework that incorporates domain generalization (DG), jointly leveraging both acoustic and textual modalities. The prop...

Distinguishing clinically diagnosed psychiatric patients from screen-positive college students: An interpretable machine learning study based on the SCL-90.

BACKGROUND: The psychiatric burden among college students has escalated substantially. However, traditional campus-based screening programs remain hampered by high false-positive rates and limited psychiatric referral resources. This study developed an interpretable machine learning framework to distinguish screen-positive students from university-aged patients with clinically diagnosed psychiatri...

Digital doppelgangers in psychiatry.

Digital doppelgangers are individualized, continuously updated digital representations of a person constructed from behavioral, physiological, and con...

Adjustment set selection for estimating optimal treatment rules under confounding.

There is an increasing call for individualized treatment rules, which leverage individual patient characteristics to recommend treatments or intervent...

Rapid Development and Testing of Behavioral Text Message Reminders for Antidepressant Adherence via Online Panels: Survey Study.

BACKGROUND: SMS text message reminders have been used to promote many health behaviors, such as improving diet and physical activity, managing chronic...

Gut Microbiome in Depression with and without REM Sleep Behavior Disorder.

Major depressive disorder (MDD) is a risk factor for neurodegeneration, yet its heterogeneity makes identifying at-risk subtype challenging. Notably, ...

Artificial intelligence-driven environments as emerging contexts in suicide risk.

Recent reports have described suicides temporally associated with artificial intelligence (AI)-mediated interactions, including conversational agents ...

Identification and validation of an explainable screening model of college students' mental health with therapeutic strategy implications.

Mental health issues, especially depressive symptoms, among young adults represent a public health challenge. Conventional psychological assessment to...

Video-Algorithmic Patient Monitoring in Mental Health Inpatient Settings: Qualitative Study of Patient or Consumer, Clinician, and Vendor Perspectives.

BACKGROUND: Video-algorithmic patient monitoring (VAPM) combines remote, noncontact sensors and algorithmic analysis and is increasingly trialed in ac...

Screening of shared molecular markers between cervical cancer and major depressive disorder and the mechanism of CRAT/CLIC4 regulating EMT in cervical cancer cells.

BACKGROUND: Cervical cancer (CC) ranks among the most prevalent malignant neoplasms affecting women worldwide. Tumor recurrence, distant metastases, a...

Development and temporal validation of a machine-learning based risk prediction model for depression in older adults with chewing difficulty: evidence from the KNHANES.

BACKGROUND: Depression in older adults is a multifactorial condition influenced by demographic, behavioral, and health-related factors. Oral functiona...

Model Development and Feasibility of Real-World Deployment of Multimodal Input-Based Subtyping of Depression in Tele-Counseling for Scalable Mental Health Assessment.

The rapid growth of tele-counseling and the use of lay counselors in high-volume, low-resource mental health services has created a need for scalable ...

Machine learning evaluation of clinical, social and behavioural factors influencing progression from pre-diabetes to type 2 diabetes: a retrospective cohort study in southeast Michigan.

OBJECTIVE: We aimed to use machine learning (ML) models to investigate the impact of clinical, social and behavioural factors on 1-year progression fr...

Integration of cognitive behavioral therapy and mobile health applications among university students with depression: a qualitative study.

BACKGROUND: Most applications for depression lack comprehensive theoretical integration and qualitative assessments of university students' needs rema...

Activation of sigma non-opioid intracellular receptor 1 (S1R) receptor inhibits plexin domain containing 2 (Plxdc2) driven microglial inflammation to ameliorate depressive-like behaviours in mice.

BACKGROUND AND PURPOSE: Microglia are central regulators of neuroinflammation in depression. Drivers involved remain incompletely understood. Sigma no...

Acute Aluminum Phosphide Poisoning: Evolving Perspectives on Toxic Mechanisms and Evidence-Based Clinical Management.

Aluminum phosphide (AlP) is a chemical compound that is used as a pesticide for suicidal purposes and can cause death, and it poses a challenge to hea...

Passive Screening for Depressive Symptoms Using Daily Wrist Actigraphy and Deep Learning: Model Development and Validation Study.

BACKGROUND: Depressive symptoms are common yet often underrecognized in routine care, underscoring the need for scalable screening approaches beyond e...

Complex dynamics in psychological data: Mapping individual symptom trajectories to group-level patterns.

This study integrates causal inference, graph analysis, temporal complexity measures, and machine learning to examine whether individual symptom traje...

Predicting comorbid anxiety in adolescents with major depressive disorder: an EEG-based machine learning approach with SHAP interpretability.

Comorbid anxiety in adolescents with major depressive disorder (adMDD) is linked to higher suicide risk and poorer prognosis, necessitating precise sc...

Integrative multi-omics reveals MHC class II-mediated neuroinflammation and systemic metabolic dysregulation as transdiagnostic drivers in major brain disorders.

Psychiatric, neurodevelopmental, and neurodegenerative disorders, including Alzheimer's disease (AD), attention-deficit/hyperactivity disorder (ADHD),...