Bipolar Disorder

Individualized prediction of depressive disorder in the elderly: A multitask deep learning approach.

INTRODUCTION: Depressive disorder is one of the major public health problems among the elderly. An effective depression risk prediction model can provide insights on the disease progression and potentially inform timely targeted interventions. Therefore, research on predicting the onset of depressive disorder for elderly adults considering the sequential progression patterns is critically needed.

Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force.

OBJECTIVES: The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD.

Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using P300 features.

BACKGROUND: The development of optimal classification criteria for specific mental disorders which share similar symptoms is an important issue for pr...

Symptomatology differences of major depression in psychiatric versus general hospitals: A machine learning approach.

BACKGROUND: Symptomatology differences of major depressive disorder (MDD) in psychiatric and general hospitals in China leads to possible misdiagnosis...

Can Machine Learning help us in dealing with treatment resistant depression? A review.

BACKGROUND: About one third of patients treated with antidepressant do not show sufficient symptoms relief and up to 15% of patients remain symptomati...

Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol.

BACKGROUND: Major depressive disorder (MDD) is one of the leading causes of disability; however, current MDD diagnosis methods lack an objective asses...

A difference degree test for comparing brain networks.

Recently, there has been a proliferation of methods investigating functional connectivity as a biomarker for mental disorders. Typical approaches incl...

Decomposition feature selection with applications in detecting correlated biomarkers of bipolar disorders.

Feature selection is an important initial step of exploratory analysis in biomedical studies. Its main objective is to eliminate the covariates that a...

Identifying incident dementia by applying machine learning to a very large administrative claims dataset.

Alzheimer's disease and related dementias (ADRD) are highly prevalent conditions, and prior efforts to develop predictive models have relied on demogr...

Application of machine learning to structural connectome to predict symptom reduction in depressed adolescents with cognitive behavioral therapy (CBT).

PURPOSE: Adolescent major depressive disorder (MDD) is a highly prevalent, incapacitating and costly illness. Many depressed teens do not improve with...

The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis.

Artificial intelligence (AI)-based techniques have been widely applied in depression research and treatment. Nonetheless, there is currently no system...

Machine-learning based brain age estimation in major depression showing no evidence of accelerated aging.

Molecular biological findings indicate that affective disorders are associated with processes akin to accelerated aging of the brain. The use of the B...

Early Detection of Depression: Social Network Analysis and Random Forest Techniques.

BACKGROUND: Major depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people gl...

SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse.

Synapses are fundamental information-processing units of the brain, and synaptic dysregulation is central to many brain disorders ("synaptopathies"). ...

Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal.

BACKGROUND: Prediction of therapeutic outcome of repetitive transcranial magnetic stimulation (rTMS) treatment is an important purpose that eliminates...

Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.

A psychological disorder is a mutilation state of the body that intervenes the imperative functioning of the mind or brain. In the last few years, the...

See your mental state from your walk: Recognizing anxiety and depression through Kinect-recorded gait data.

As the challenge of mental health problems such as anxiety and depression increasing today, more convenient, objective, real-time assessing techniques...

Long-term results of monopolar versus bipolar radiofrequency ablation procedure for atrial fibrillation.

BACKGROUND: In this study, we aimed to evaluate the long-term outcomes of monopolar or bipolar radiofrequency ablation concomitant to mitral valve sur...

Selection and Optimization of Temporal Spike Encoding Methods for Spiking Neural Networks.

Spiking neural networks (SNNs) receive trains of spiking events as inputs. In order to design efficient SNN systems, real-valued signals must be optim...

Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: A resting state electroencephalography study.

BACKGROUND: Repetitive transcranial magnetic stimulation (TMS) is clinically effective for major depressive disorder (MDD) and investigational for oth...