Bipolar Disorder

Relative importance of symptoms, cognition, and other multilevel variables for psychiatric disease classifications by machine learning.

This study used machine-learning algorithms to make unbiased estimates of the relative importance of various multilevel data for classifying cases with schizophrenia (n = 60), schizoaffective disorder (n = 19), bipolar disorder (n = 20), unipolar depression (n = 14), and healthy controls (n = 51) into psychiatric diagnostic categories. The Random Forest machine learning algorithm, which showed bes...

Artificial intelligence based discovery of the association between depression and chronic fatigue syndrome.

BACKGROUND: Both of the modern medicine and the traditional Chinese medicine classify depressive disorder (DD) and chronic fatigue syndrome (CFS) to one type of disease. Unveiling the association between depressive and the fatigue diseases provides a great opportunity to bridge the modern medicine with the traditional Chinese medicine.

Robust water-fat separation for multi-echo gradient-recalled echo sequence using convolutional neural network.

PURPOSE: To accurately separate water and fat signals for bipolar multi-echo gradient-recalled echo sequence using a convolutional neural network (CNN...

Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder.

Brain imaging studies have revealed that functional and structural brain connectivity in the so-called triple network (i.e., default mode network (DMN...

Electroconvulsive Therapy Induces Cortical Morphological Alterations in Major Depressive Disorder Revealed with Surface-Based Morphometry Analysis.

Although electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder (MDD), the mechanism underlying the the...

Outcome-Weighted Learning for Personalized Medicine with Multiple Treatment Options.

To achieve personalized medicine, an individualized treatment strategy assigning treatment based on an individual's characteristics that leads to the ...

Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data.

Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or ea...

A Group Decision Making Framework Based on Neutrosophic TOPSIS Approach for Smart Medical Device Selection.

Advances in the medical industry has become a major trend because of the new developments in information technologies. This research offers a novel ap...

Predicting persistent depressive symptoms in older adults: A machine learning approach to personalised mental healthcare.

BACKGROUND: Depression causes significant physical and psychosocial morbidity. Predicting persistence of depressive symptoms could permit targeted pre...

A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder.

Mobile technologies offer new opportunities for prospective, high resolution monitoring of long-term health conditions. The opportunities seem of part...

Predicting instructed simulation and dissimulation when screening for depressive symptoms.

The intentional distortion of test results presents a fundamental problem to self-report-based psychiatric assessment, such as screening for depressiv...

Ascertaining Depression Severity by Extracting Patient Health Questionnaire-9 (PHQ-9) Scores from Clinical Notes.

The Patient Health Questionnaire-9 (PHQ-9) is a validated instrument for assessing depression severity. While some electronic health record (EHR) syst...

Self-limited single nanowire systems combining all-in-one memristive and neuromorphic functionalities.

The ability for artificially reproducing human brain type signals' processing is one of the main challenges in modern information technology, being on...

Machine learning studies on major brain diseases: 5-year trends of 2014-2018.

In the recent 5 years (2014-2018), there has been growing interest in the use of machine learning (ML) techniques to explore image diagnosis and progn...

Hyponatremia Presenting with Recurrent Mania.

Primary psychogenic polydipsia (PPD) is a chronic, relapsing condition in which there is a disturbance in thirst control primarily due to an underlyin...

Predominant polarity classification and associated clinical variables in bipolar disorder: A machine learning approach.

BACKGROUND: Bipolar disorder (BD) is a severe psychiatric disorder characterized by periodic episodes of manic and depressive symptomatology. Predomin...

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, however, are based on group comparisons with unknown t...

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 well-being, and it is often concomitant with anxie...

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 known to cause lower urinary tract symptoms (LUTS) tha...

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 affects about 10% of the population worldwide. Cur...