AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Depressive Disorder, Major

Showing 101 to 110 of 190 articles

Clear Filters

Construction of gene-classifier and co-expression network analysis of genes in association with major depressive disorder.

Psychiatry research
Because the pathogenesis of major depressive disorder (MDD) is still unclear and the accurate diagnosis remains unavailable, we aimed to analyze its molecular mechanisms and develop a gene classifier to improve diagnostic accuracy. We extracted diffe...

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

Psychological medicine
BACKGROUND: Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and s...

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

Sensors (Basel, Switzerland)
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 tr...

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

Clinical EEG and neuroscience
The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effect...

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

Journal of psychosomatic research
OBJECTIVE: Individuals with immune-mediated inflammatory disease (IMID) have a higher prevalence of psychiatric disorders than the general population. We utilized machine-learning to identify patient-reported outcome measures (PROMs) that accurately ...

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions.

Bipolar disorders
OBJECTIVES: The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic and prognostic tools, as well as identifying biologically valid subt...

Identifying the presence and timing of discrete mood states prior to therapy.

Behaviour research and therapy
The present study tested a novel, person-specific method for identifying discrete mood profiles from time-series data, and examined the degree to which these profiles could be predicted by lagged mood and anxiety variables and time-based variables, i...

Predicting individual clinical trajectories of depression with generative embedding.

NeuroImage. Clinical
Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift recovery, others show relapsing-remitting or chronic courses. Predicting individual cl...

Deep learning-based automated speech detection as a marker of social functioning in late-life depression.

Psychological medicine
BACKGROUND: Late-life depression (LLD) is associated with poor social functioning. However, previous research uses bias-prone self-report scales to measure social functioning and a more objective measure is lacking. We tested a novel wearable device ...