Depression

Graphene-based FETs for advanced biocatalytic profiling: investigating heme peroxidase activity with machine learning insights.

Graphene-based field-effect transistors (GFETs) are rapidly gaining recognition as powerful tools for biochemical analysis due to their exceptional sensitivity and specificity. In this study, we utilize a GFET system to explore the peroxidase-based biocatalytic behavior of horseradish peroxidase (HRP) and the heme molecule, the latter serving as the core component responsible for HRP's enzymatic a...

The role of senescence-related genes in major depressive disorder: insights from machine learning and single cell analysis.

BACKGROUND: Evidence indicates that patients with Major Depressive Disorder (MDD) exhibit a senescence phenotype or an increased susceptibility to premature senescence. However, the relationship between senescence-related genes (SRGs) and MDD remains underexplored.

Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study.

BACKGROUND: Depression, characterized by persistent sadness and loss of interest in daily activities, greatly reduces quality of life. Early detection...

Comprehensive evaluation of pipelines for classification of psychiatric disorders using multi-site resting-state fMRI datasets.

Objective classification biomarkers that are developed using resting-state functional magnetic resonance imaging (rs-fMRI) data are expected to contri...

Social media content and suicidality: Implications for practice.

Artificial intelligence is a useful tool for examining suicidality on social media, where people share their thoughts. However, existing research has ...

Validation of a machine learning model for indirect screening of suicidal ideation in the general population.

Suicide is among the leading causes of death worldwide and a concerning public health problem, accounting for over 700,000 registered deaths worldwide...

Optimizing depression detection in clinical doctor-patient interviews using a multi-instance learning framework.

In recent years, the number of people suffering from depression has gradually increased, and early detection is of great significance for the well-bei...

Neurobiologically interpretable causal connectome for predicting young adult depression: A graph neural network study.

BACKGROUND: There is a surprising lack of neuroimaging studies of depression that not only identify the whole brain causal connectivity features but a...

Development and validation of short-term, medium-term, and long-term suicide attempt prediction models based on a prospective cohort in Korea.

BACKGROUND: This study aimed to develop and validate prediction models for short-(3 months), medium-(1 year), and long-term suicide attempts among hig...

Machine learning based seizure classification and digital biosignal analysis of ECT seizures.

While artificial intelligence has received considerable attention in various medical fields, its application in the field of electroconvulsive therapy...

Identifying major depressive disorder among US adults living alone using stacked ensemble machine learning algorithms.

BACKGROUND: It has been increasingly recognized that adults living alone have a higher likelihood of developing Major Depressive Disorder (MDD) than t...

Proteome analysis of the prefrontal cortex and the application of machine learning models for the identification of potential biomarkers related to suicide.

INTRODUCTION: Suicide is a significant public health problem, with increased rates in low- and middle-income countries such as Mexico; therefore, suic...

Predicting Treatment Response of Repetitive Transcranial Magnetic Stimulation in Major Depressive Disorder Using an Explainable Machine Learning Model Based on Electroencephalography and Clinical Features.

Major depressive disorder (MDD) is highly heterogeneous in response to repetitive transcranial magnetic stimulation (rTMS), and identifying predictive...

Prediction of depressive disorder using machine learning approaches: findings from the NHANES.

BACKGROUND: Depressive disorder, particularly major depressive disorder (MDD), significantly impact individuals and society. Traditional analysis meth...

Comparison of logistic regression and machine learning methods for predicting depression risks among disabled elderly individuals: results from the China Health and Retirement Longitudinal Study.

BACKGROUND: Given the accelerated aging population in China, the number of disabled elderly individuals is increasing, and depression is a common ment...

Differentiating adolescent suicidal and nonsuicidal self-harm with artificial intelligence: Beyond suicidal intent and capability for suicide.

Clinical differentiation between adolescent suicidal self-harm (SSH) and nonsuicidal self-harm (NSSH) is a significant challenge for mental health pro...

Efficacy and effectiveness of therapist-guided internet versus face-to-face cognitive behavioural therapy for depression via counterfactual inference using naturalistic registers and machine learning in Finland: a retrospective cohort study.

BACKGROUND: According to meta-analyses of randomised controlled trials (RCTs), therapist-guided internet-delivered cognitive behavioural therapy (iCBT...

Identifying Adolescent Depression and Anxiety Through Real-World Data and Social Determinants of Health: Machine Learning Model Development and Validation.

BACKGROUND: The prevalence of adolescent mental health conditions such as depression and anxiety has significantly increased. Despite the potential of...

EEG Temporal-Spatial Feature Learning for Automated Selection of Stimulus Parameters in Electroconvulsive Therapy.

The risk of adverse effects in Electroconvulsive Therapy (ECT), such as cognitive impairment, can be high if an excessive stimulus is applied to induc...

Predictors of depression among Chinese college students: a machine learning approach.

BACKGROUND: Depression is highly prevalent among college students, posing a significant public health challenge. Identifying key predictors of depress...