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Depressive Disorder

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Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample.

PloS one
BACKGROUND: Depression is commonly comorbid with many other somatic diseases and symptoms. Identification of individuals in clusters with comorbid symptoms may reveal new pathophysiological mechanisms and treatment targets. The aim of this research w...

Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM).

European psychiatry : the journal of the Association of European Psychiatrists
BACKGROUND: Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depress...

Technology for Early Detection of Depression and Anxiety in Older People.

Studies in health technology and informatics
Under-diagnosis of depression and anxiety is common in older adults. This project took a mixed methods approach to explore the application of machine learning and technology for early detection of these conditions. Mood measures collected with digita...

Getting RID of the blues: Formulating a Risk Index for Depression (RID) using structural equation modeling.

The Australian and New Zealand journal of psychiatry
OBJECTIVE: While risk factors for depression are increasingly known, there is no widely utilised depression risk index. Our objective was to develop a method for a flexible, modular, Risk Index for Depression using structural equation models of key d...

Depression recognition according to heart rate variability using Bayesian Networks.

Journal of psychiatric research
BACKGROUND: Doctors mainly use scale tests and subjective judgment in the clinical diagnosis of depression. Researches have demonstrated that depression is associated with the dysfunction of the autonomic nervous system (ANS), where its modulation ca...

Automatic Detection of Depression by Using a Neural Network.

Studies in health technology and informatics
Depression is the most common psychiatric disorder worldwide, which affects more than 300 million people. We aimed to detect depressed patients and healthy people automatically. We work on the PHQ-9 questionnaires and reduced it to a PHQ-5 questionna...

Predicting suicidal ideation in primary care: An approach to identify easily assessable key variables.

General hospital psychiatry
OBJECTIVE: To obtain predictors of suicidal ideation, which can also be used for an indirect assessment of suicidal ideation (SI). To create a classifier for SI based on variables of the Patient Health Questionnaire (PHQ) and sociodemographic variabl...

Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review.

Journal of affective disorders
BACKGROUND: No previous study has comprehensively reviewed the application of machine learning algorithms in mood disorders populations. Herein, we qualitatively and quantitatively evaluate previous studies of machine learning-devised models that pre...

Ensemble machine learning prediction of posttraumatic stress disorder screening status after emergency room hospitalization.

Journal of anxiety disorders
Posttraumatic stress disorder (PTSD) develops in a substantial minority of emergency room admits. Inexpensive and accurate person-level assessment of PTSD risk after trauma exposure is a critical precursor to large-scale deployment of early intervent...