Psychiatry

Depression

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

1,366 articles
Stay Ahead - Weekly Depression research updates
Subscribe
Browse Categories
Showing 862-882 of 1,366 articles
Rich spectrum of neural field dynamics in the presence of short-term synaptic depression.

In continuous attractor neural networks (CANNs), spatially continuous information such as orientatio...

Analysis of NTSB Aircraft-Assisted Pilot Suicides: 1982-2014.

On March 24, 2015, a Germanwings aircraft crashed in the Alps. The suicidal copilot killed himself a...

A Controlled Trial Using Natural Language Processing to Examine the Language of Suicidal Adolescents in the Emergency Department.

What adolescents say when they think about or attempt suicide influences the medical care they recei...

Changes in depression stigma after the Germanwings crash - Findings from German population surveys.

BACKGROUND: Media coverage of the Germanwings plane crash intensely focused on the co-pilot's mental...

Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression.

Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neur...

PsyGeNET: a knowledge platform on psychiatric disorders and their genes.

UNLABELLED: PsyGeNET (Psychiatric disorders and Genes association NETwork) is a knowledge platform f...

Negative Affect Instability among Individuals with Comorbid Borderline Personality Disorder and Posttraumatic Stress Disorder.

Ecological momentary assessment (EMA; Stone & Shiffman, 1994) was utilized to examine affective inst...

Screening Internet forum participants for depression symptoms by assembling and enhancing multiple NLP methods.

Depression is a disease that can dramatically lower quality of life. Symptoms of depression can rang...

A Mobile Health Application to Predict Postpartum Depression Based on Machine Learning.

BACKGROUND: Postpartum depression (PPD) is a disorder that often goes undiagnosed. The development o...

Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction.

OBJECTIVE: Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and tr...

Spontaneous motion on two-dimensional continuous attractors.

Attractor models are simplified models used to describe the dynamics of firing rate profiles of a po...

Using support vector machines to identify protein phosphorylation sites in viruses.

Phosphorylation of viral proteins plays important roles in enhancing replication and inhibition of n...

Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach.

Feature selection is an important step in many pattern recognition systems aiming to overcome the so...

Decoding chronic stress: From behavioral-molecular dynamics in mice to clinical implications of cortisol and IL-17 in depression severity.

BACKGROUNDS: The etiology of depression involves chronic stress, a recognized determinant of onset a...

AI-Driven Discovery and Optimization of Positive Allosteric Modulators for NMDA Receptors: Potential Applications in Depression.

-Methyl-d-aspartate receptors (NMDARs) are extensively distributed throughout the central nervous sy...

Machine-Learning-Based Prediction of Suicide Risk Using Preliminary Questionnaire and Consultation Text.

In Japan, chat-based mental health counseling services have low response rates due to understaffing....

Predicting Postpartum Depression Risk Using Social Determinants of Health.

Postpartum depression (PPD) affects approximately 20% of women after childbirth and has complex etio...

Predicting Antidepressant Deprescription with Machine Learning Using Administrative Data.

The high prevalence of failed antidepressant deprescription attempts makes it difficult for clinicia...

Browse Categories