Depression

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 brain diseases. However, major depressive disorder may also cause temporary deterioration of one's cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although...

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 cognitive characteristics. There is a fundamental...

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 and how patients will respond to antidepressant m...

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 presenting with: unusually abrupt onset of obsessive co...

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, feels and behaves. The major reason behind wrong d...

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 phenotypes, a novel prediction model for depressive sympto...

Supervised Machine Learning: A Brief Primer.

Machine learning is increasingly used in mental health research and has the potential to advance our understanding of how to characterize, predict, an...

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 unplanned urbanization, sedentary lifestyle, econo...

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 of people with epilepsy (PWE) outside of formal c...

Towards a brain-based predictome of mental illness.

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognit...

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 psychiatric disorders than the general population....

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

OBJECTIVE: Machine learning algorithms in electronic medical records can classify patients by suicide risk, but no research has explored clinicians' p...

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 trauma and stressful life events. However, such studie...

Machine Learning Based Suicide Ideation Prediction for Military Personnel.

Military personnel have greater psychological stress and are at higher suicide attempt risk compared with the general population. High mental stress m...

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. Our model produces two types of sequences, corre...

Towards a fully automated surveillance of well-being status in laboratory mice using deep learning: Starting with facial expression analysis.

Assessing the well-being of an animal is hindered by the limitations of efficient communication between humans and animals. Instead of direct communic...

A social robot intervention on depression, loneliness, and quality of life for Taiwanese older adults in long-term care.

OBJECTIVES: To investigate the effect of a social robot intervention on depression, loneliness, and quality of life of older adults in long-term care ...

Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample.

BACKGROUND: This study aimed to determine conditional dependence relationships of variables that contribute to psychological vulnerability associated ...

Using machine learning to classify suicide attempt history among youth in medical care settings.

BACKGROUND: The current study aimed to classify recent and lifetime suicide attempt history among youth presenting to medical settings using machine l...