AIMC Topic: Depressive Disorder

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Deconstructing depression by machine learning: the POKAL-PSY study.

European archives of psychiatry and clinical neuroscience
Unipolar depression is a prevalent and disabling condition, often left untreated. In the outpatient setting, general practitioners fail to recognize depression in about 50% of cases mainly due to somatic comorbidities. Given the significant economic,...

Predicting Outcomes of Antidepressant Treatment in Community Practice Settings.

Psychiatric services (Washington, D.C.)
OBJECTIVE: The authors examined whether machine-learning models could be used to analyze data from electronic health records (EHRs) to predict patients' responses to antidepressant medications.

Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning.

Sensors (Basel, Switzerland)
Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person's psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and sub...

Evaluation of Effect of Curcumin on Psychological State of Patients with Pulmonary Hypertension by Magnetic Resonance Image under Deep Learning.

Contrast media & molecular imaging
This research aimed to evaluate the right ventricular segmentation ability of magnetic resonance imaging (MRI) images based on deep learning and evaluate the influence of curcumin (Cur) on the psychological state of patients with pulmonary hypertensi...

Convolutional Neural Network-Based Deep Learning Model for Predicting Differential Suicidality in Depressive Patients Using Brain Generalized q-Sampling Imaging.

The Journal of clinical psychiatry
OBJECTIVE: Suicide is a priority health problem. Suicide assessment depends on imperfect clinician assessment with minimal ability to predict the risk of suicide. Machine learning/deep learning provides an opportunity to detect an individual at risk ...

A peripheral inflammatory signature discriminates bipolar from unipolar depression: A machine learning approach.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: Mood disorders (major depressive disorder, MDD, and bipolar disorder, BD) are considered leading causes of life-long disability worldwide, where high rates of no response to treatment or relapse and delays in receiving a proper diagnosis ...

Precision psychiatry in clinical practice.

International journal of psychiatry in clinical practice
The treatment of depression represents a major challenge for healthcare systems and choosing among the many available drugs without objective guidance criteria is an error-prone process. Recently, pharmacogenetic biomarkers entered in prescribing gui...

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

Journal of medical systems
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 diagnosis of depression is absence of any laboratory test for detection as well as severity scaling o...

Fitting prediction rule ensembles to psychological research data: An introduction and tutorial.

Psychological methods
Prediction rule ensembles (PREs) are a relatively new statistical learning method, which aim to strike a balance between predictive performance and interpretability. Starting from a decision tree ensemble, like a boosted tree ensemble or a random for...

Developing algorithms to predict adult onset internalizing disorders: An ensemble learning approach.

Journal of psychiatric research
A growing literature is utilizing machine learning methods to develop psychopathology risk algorithms that can be used to inform preventive intervention. However, efforts to develop algorithms for internalizing disorder onset have been limited. The g...