Psychiatry

Depression

Latest AI and machine learning research in depression for healthcare professionals.

1,353 articles
Stay Ahead - Weekly Depression research updates
Subscribe
Browse Specialties
Showing 358-378 of 1,353 articles
Individual Predictors of Response to A Behavioral Activation-Based Digital Smoking Cessation Intervention: A Machine Learning Approach.

Depression is prevalent among individuals who smoke cigarettes and increases risk for relapse. A pr...

Machine learning analysis with population data for prepregnancy and perinatal risk factors for the neurodevelopmental delay of offspring.

Neurodevelopmental disorders (NDD) in offspring are associated with a complex combination of pre-and...

An organic brain-inspired platform with neurotransmitter closed-loop control, actuation and reinforcement learning.

Organic neuromorphic platforms have recently received growing interest for the implementation and in...

Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review.

OBJECTIVES: This study underscores the importance of exploring AI's creative applications in treatin...

Brain-computer interfaces inspired spiking neural network model for depression stage identification.

BACKGROUND: Depression is a global mental disorder, and traditional diagnostic methods mainly rely o...

Using AI-Based Virtual Companions to Assist Adolescents with Autism in Recognizing and Addressing Cyberbullying.

Social media platforms and online gaming sites play a pervasive role in facilitating peer interactio...

GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals.

Identifying major depressive disorder (MDD) using objective physiological signals has become a press...

Mining key circadian biomarkers for major depressive disorder by integrating bioinformatics and machine learning.

OBJECTIVE: This study aimed to identify key clock genes closely associated with major depressive dis...

Machine Learning in Electroconvulsive Therapy: A Systematic Review.

Despite years of research, we are still not able to reliably predict who might benefit from electroc...

Mental health analysis of international students using machine learning techniques.

International students' mental health has become an increasing concern in recent years, as more stud...

Identifying the risk of depression in a large sample of adolescents: An artificial neural network based on random forest.

BACKGROUND: This study aims to develop an artificial neural network (ANN) prediction model incorpora...

Machine learning identifies different related factors associated with depression and suicidal ideation in Chinese children and adolescents.

BACKGROUND: Depression and suicidal ideation often co-occur in children and adolescents, yet they po...

Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms.

In contemporary society, depression has emerged as a prominent mental disorder that exhibits exponen...

EEG based functional connectivity in resting and emotional states may identify major depressive disorder using machine learning.

OBJECTIVE: Disrupted brain network connectivity underlies major depressive disorder (MDD). Altered E...

Graph convolutional network with attention mechanism improve major depressive depression diagnosis based on plasma biomarkers and neuroimaging data.

BACKGROUND: The absence of clinically-validated biomarkers or objective protocols hinders effective ...

Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU.

INTRODUCTION: Neonatal intensive care unit (NICU) admission is a stressful experience for parents. N...

Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies.

OBJECTIVE: Suicide is a complex and multifactorial public health problem. Understanding and addressi...

An automated approach for predicting HAMD-17 scores via divergent selective focused multi-heads self-attention network.

This study introduces the Divergent Selective Focused Multi-heads Self-Attention Network (DSFMANet),...

Browse Specialties