Psychiatry

Bipolar Disorder

Latest AI and machine learning research in bipolar disorder for healthcare professionals.

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Showing 463-483 of 830 articles
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 con...

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 radio...

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 effici...

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...

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 dis...

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 ...

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-call...

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 depressiv...

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

To achieve personalized medicine, an individualized treatment strategy assigning treatment based on ...

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...

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 informa...

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

BACKGROUND: Depression causes significant physical and psychosocial morbidity. Predicting persistenc...

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...

Predicting instructed simulation and dissimulation when screening for depressive symptoms.

The intentional distortion of test results presents a fundamental problem to self-report-based psych...

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 severi...

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 cha...

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 (M...

Hyponatremia Presenting with Recurrent Mania.

Primary psychogenic polydipsia (PPD) is a chronic, relapsing condition in which there is a disturban...

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 episode...

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...

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