Bipolar Disorder

Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach.

Many variables have been linked to different course trajectories of depression. These findings, howe...

Feasibility and Acceptability of Mobile Phone-Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults.

BACKGROUND: Chronic pain is a globally prevalent condition. It is closely linked with psychological ...

A Study of the Role of 180W XPS Lithium Triborate Laser in the Treatment of Patients With Lower Urinary Tracts Symptoms Due to Benign Prostatic Hyperplasia.

Benign prostatic hyperplasia (BPH) is a disease of the prostate commonly seen in elderly males know...

Machine learning in major depression: From classification to treatment outcome prediction.

AIMS: Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and ...

A rapid LC-MS/MS method for simultaneous determination of quetiapine and duloxetine in rat plasma and its application to pharmacokinetic interaction study.

Combinations of new antidepressants like duloxetine and second-generation antipsychotics like quetia...

Self-Efficacy, Poststroke Depression, and Rehabilitation Outcomes: Is There a Correlation?

BACKGROUND: The sudden live changes of stroke survivors may lead to negative psychological and behav...

Multiday EMG-Based Classification of Hand Motions with Deep Learning Techniques.

Pattern recognition of electromyography (EMG) signals can potentially improve the performance of myo...

Is there a symptomatic distinction between the affective psychoses and schizophrenia? A machine learning approach.

Dubiety exists over whether clinical symptoms of schizophrenia can be distinguished from affective p...

Ab Initio Calculations of the Redox Potentials of Additives for Lithium-Ion Batteries and Their Prediction through Machine Learning.

Ab initio molecular orbital calculations were carried out to examine the redox potentials of 149 ele...

Disrupted functional connectivity within the default mode network and salience network in unmedicated bipolar II disorder.

BACKGROUND: Recent studies demonstrate that functional disruption in resting-state networks contribu...

On the importance of hidden bias and hidden entropy in representational efficiency of the Gaussian-Bipolar Restricted Boltzmann Machines.

In this paper, we analyze the role of hidden bias in representational efficiency of the Gaussian-Bip...

Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study.

Identification of risk factors of treatment resistance may be useful to guide treatment selection, a...

Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens.

Dynamic treatment regimens (DTRs) are sequential treatment decisions tailored by patient's evolving ...

Machine learning improved classification of psychoses using clinical and biological stratification: Update from the bipolar-schizophrenia network for intermediate phenotypes (B-SNIP).

Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach.

Neuroimaging studies have been steadily explored in Bipolar Disorder (BD) in the last decades. Neuro...

Emotional hyper-reactivity and cardiometabolic risk in remitted bipolar patients: a machine learning approach.

OBJECTIVE: Remitted bipolar disorder (BD) patients frequently present with chronic mood instability ...

A morphometric signature of depressive symptoms in unmedicated patients with mood disorders.

OBJECTIVE: A growing literature indicates that unipolar depression and bipolar depression are associ...

Automated EEG-based screening of depression using deep convolutional neural network.

In recent years, advanced neurocomputing and machine learning techniques have been used for Electroe...

Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning.

We sought to test the hypothesis that transcriptome-level gene signatures are differentially expres...

Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder.

Electroconvulsive therapy (ECT) is one of the most effective treatments for major depression disorde...

A YinYang bipolar fuzzy cognitive TOPSIS method to bipolar disorder diagnosis.

BACKGROUND AND OBJECTIVE: Bipolar disorder is often mis-diagnosed as unipolar depression in the clin...