Depression

Caregiver burden in stroke inpatients: a randomized study comparing robot-assisted gait training and conventional therapy.

The effects of caregiver burden during the inpatient rehabilitation period have not yet been investi...

Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood.

BACKGROUND: Visual and auditory signs of patient functioning have long been used for clinical diagno...

Test-Retest Reliability of Kinematic Assessments for Upper Limb Robotic Rehabilitation.

Robot-measured kinematic variables are increasingly used in neurorehabilitation to characterize moto...

Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach.

Deep learning techniques have recently made considerable advances in the field of artificial intelli...

Canada protocol: An ethical checklist for the use of artificial Intelligence in suicide prevention and mental health.

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients.

Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brai...

Prediction of depression cases, incidence, and chronicity in a large occupational cohort using machine learning techniques: an analysis of the ELSA-Brasil study.

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Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach.

The human brain is characterized by complex structural, functional connections that integrate unique...

Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial.

IMPORTANCE: Despite the high prevalence and potential outcomes of major depressive disorder, whether...

Pediatric Acute-Onset Neuropsychiatric Syndrome: A Data Mining Approach to a Very Specific Constellation of Clinical Variables.

Pediatric acute onset neuropsychiatric syndrome (PANS) is a clinically heterogeneous disorder prese...

Detection of Depression and Scaling of Severity Using Six Channel EEG Data.

Depression is a psychiatric problem which affects the growth of a person, like how a person thinks, ...

Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection.

To solve major limitations in algorithms for the metabolite-based prediction of psychiatric phenotyp...

Supervised Machine Learning: A Brief Primer.

Machine learning is increasingly used in mental health research and has the potential to advance our...

Identification of Risk Factors Associated with Obesity and Overweight-A Machine Learning Overview.

Social determining factors such as the adverse influence of globalization, supermarket growth, fast ...

Digital conversations about suicide among teenagers and adults with epilepsy: A big-data, machine learning analysis.

OBJECTIVE: Digital media conversations can provide important insight into the concerns and struggles...

Towards a brain-based predictome of mental illness.

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years ...

Machine-learning models for depression and anxiety in individuals with immune-mediated inflammatory disease.

OBJECTIVE: Individuals with immune-mediated inflammatory disease (IMID) have a higher prevalence of ...

Association Rule Learning Is an Easy and Efficient Method for Identifying Profiles of Traumas and Stressors that Predict Psychopathology in Disaster Survivors: The Example of Sri Lanka.

Research indicates that psychopathology in disaster survivors is a function of both experienced trau...

Machine Learning Algorithms in Suicide Prevention: Clinician Interpretations as Barriers to Implementation.

OBJECTIVE: Machine learning algorithms in electronic medical records can classify patients by suicid...

Machine Learning Based Suicide Ideation Prediction for Military Personnel.

Military personnel have greater psychological stress and are at higher suicide attempt risk compared...

Neuronal mechanisms for sequential activation of memory items: Dynamics and reliability.

In this article we present a biologically inspired model of activation of memory items in a sequence...