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Depressive Disorder, Major

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Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study.

Brain and behavior
BACKGROUND: Generalized anxiety disorder (GAD) is difficult to recognize and hard to separate from major depression (MD) in clinical settings. Biomarkers might support diagnostic decisions. This study used machine learning on multimodal biobehavioral...

Predictive diagnosis of major depression using NMR-based metabolomics and least-squares support vector machine.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND: Major depressive (MD) disorder is a serious psychiatric disorder that can result in suicidal behavior if not treated. The MD diagnosis using a standardized instrument instead of a structured interview will be advantageous for treatment an...

Advanced literature analysis in a Big Data world.

Annals of the New York Academy of Sciences
Comprehensive data mining of the scientific literature has become an increasing challenge. To address this challenge, Elsevier's Pathway Studio software uses the techniques of natural language processing to systematically extract specific biological ...

A support vector machine model provides an accurate transcript-level-based diagnostic for major depressive disorder.

Translational psychiatry
Major depressive disorder (MDD) is a critical cause of morbidity and disability with an economic cost of hundreds of billions of dollars each year, necessitating more effective treatment strategies and novel approaches to translational research. A no...

Predicting early psychiatric readmission with natural language processing of narrative discharge summaries.

Translational psychiatry
The ability to predict psychiatric readmission would facilitate the development of interventions to reduce this risk, a major driver of psychiatric health-care costs. The symptoms or characteristics of illness course necessary to develop reliable pre...

Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder.

NeuroImage
Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals in clinically heterogeneous populations. At the very least, diagnostic markers should be able to diff...

Accuracy of automated classification of major depressive disorder as a function of symptom severity.

NeuroImage. Clinical
BACKGROUND: Growing evidence documents the potential of machine learning for developing brain based diagnostic methods for major depressive disorder (MDD). As symptom severity may influence brain activity, we investigated whether the severity of MDD ...

[Medial Stigmatization of Mentally Ill Persons after the "Germanwings"-Crash].

Psychiatrische Praxis
OBJECTIVE: The present study was designed to investigate the frequency of media stigmatization of mentally ill persons after the crash of the "Germanwings"-aircraft on March 2015.

Cross-trial prediction of treatment outcome in depression: a machine learning approach.

The lancet. Psychiatry
BACKGROUND: Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific ...

Studying depression using imaging and machine learning methods.

NeuroImage. Clinical
Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the ...