Depression

Ecological assessment of transdiagnostic clinical symptoms in serious mental illness with daily smartphone surveys.

Clinical symptoms in serious mental illness (SMI) fluctuate dynamically, yet standard interview-based assessments are limited in tracking these changes. Smartphone-based ecological surveys offer a scalable approach to monitoring symptoms in naturalistic settings. We analyzed intensive longitudinal data from 56 outpatients with psychotic or affective disorders who contributed 3901 daily surveys and...

A wearable device dataset for mental health assessment using laser Doppler flowmetry and fluorescence spectroscopy sensors.

BACKGROUND: Mental health conditions such as depression, anxiety, and stress are commonly assessed using self-reported questionnaires and limited wearable physiological measures. However, reliance on subjective reporting, restricted sensor modalities such as heart rate variability and electrodermal activity, and small or homogeneous datasets may limit generalizability. We aimed to evaluate whether...

Sedation in Acute Ischemic Stroke: Pathophysiological Basis and Evidence for Commonly Used Agents.

Sedation is frequently required in the acute management of ischemic stroke (AIS), yet its potential to influence secondary brain injury and systemic c...

Neuroimaging-derived biotypes of self-reported insomnia symptoms are differentially associated with affective symptoms.

BACKGROUND: Sleep problems are among the most prevalent health complaints during adolescence and show a reciprocal relationship with the development o...

Urinary Volatile Organic Compound Metabolites and Depressive Symptoms Among U.S. Adults With Cardiovascular-Kidney-Metabolic Syndrome Stages 0-3: NHANES-Based Associations and In Silico Multi-Omics Insights.

BACKGROUND: Depression is a major global public health burden, and environmental pollutants are increasingly associated with depressive symptoms. Vola...

Prediction of Clinically Significant Depressive Symptoms at 2-Year Follow-Up in Older Adults: Machine Learning Study Using the English Longitudinal Study of Ageing.

BACKGROUND: Depression in older adults is often underdiagnosed due to atypical symptom presentation and generational stigma, leading to delayed interv...

Reduction in suicides and suicide attempts following implementation of AI-based video surveillance in the Stockholm metro system: an intervention study.

BACKGROUND: Over 700,000 suicides occurring globally each year are a major public health issue. Railways and metros provide a lethal means of suicide,...

Cross-ancestry pleiotropic analysis of imaging-derived phenotypes enhances risk stratification of depression.

Depression arises from dynamic interactions among genetic predisposition, brain alterations, and environmental stressors. Despite genome-wide associat...

Development and validation of a machine learning model to detect psychiatric symptoms in Huntington's disease using speech analysis.

Huntington's disease (HD) causes progressive disability through motor, psychiatric, and cognitive symptoms. Machine learning speech analysis can detec...

Eros and Thanatos of the new millennium: reflections on the relationship between a young man with Asperger's syndrome and an AI chatbot from the Italian forensic medical perspective.

The authors analyze the 2024 US Court Case concerning a civil action brought by the mother of an American adolescent affected by Asperger syndrome who...

Association of lipid profiles with suicide attempts in young adults with first-episode drug-naive major depressive disorder: a gender-specific analysis.

Major depressive disorder (MDD) is strongly associated with suicide risk. While lipid metabolism dysregulation is implicated in MDD pathophysiology, i...

Trajectory analysis of sleep disorders and anxiety-depression in female breast cancer patients undergoing chemotherapy: based on group-based Multi-Trajectory Model and machine learning.

BACKGROUND: Sleep disorders and anxiety-depression symptoms can significantly impair the quality of life and treatment adherence among breast cancer p...

Innovations in Crisis Line Interventions.

Background: Crisis lines provide evidence-based suicide prevention support that often involves active listening, safety assessment, collaborative prob...

Esketamine multi-omic biomarker evaluation in major depressive disorder (EMBER-MDD): concept, objectives and methodologies of a non-clinical investigator-initiated study.

Treatment resistance (TR) in major depressive disorder (MDD) affects a substantial minority of patients and is hard to recognize early, delaying inten...

Use of a Conversational Agent for Training Mental Health Professionals in Suicide Safety Planning: Pilot Feasibility and Acceptability Study.

BACKGROUND: Safety planning is recognized as one of the most effective interventions for reducing suicidal behaviors. The quality of safety plans stro...

Therapeutic Interaction Features of AI Chatbots in Depression Interventions: Systematic Review and Meta-Analysis.

BACKGROUND: Depression is a prevalent mental health disorder and a leading cause of disability worldwide, creating substantial personal and societal b...

Machine learning-based cross-sectional exploration of depression-chronic pain associations in chronic respiratory disease patients.

BACKGROUND: Chronic respiratory diseases (CRD) are a leading global cause of death, with high comorbidity rates of depression and chronic pain forming...

AI Chatbot Suicide Risk Detection and Response: Human Validation Study of the Open-Source VERA-MH Safety Evaluation.

BACKGROUND: Millions of people now use leading generative artificial intelligence (AI) tools (chatbots) for psychological support. Despite the promise...

Graph Convolutional Neural Network based Depression Detection using Brain Functional Connectivity Measures.

The structural and functional connectivity of the brain network is a combination of complex connections and interconnections among neurons of differen...

Which machine learning algorithms are best at predicting eating disorder tendencies among nursing students in China?

BACKGROUND: Nursing students are at high risk for developing eating disorders (ED). However, methods for identifying ED tendencies within this group r...