Bipolar Disorder

Machine learning for antidepressant treatment selection in depression.

Finding the right antidepressant for the individual patient with major depressive disorder can be a difficult endeavor and is mostly based on trial-and-error. Machine learning (ML) is a promising tool to personalize antidepressant prescription. In this review, we summarize the current evidence of ML in the selection of antidepressants and conclude that its value for clinical practice is still limi...

Machine learning applied to the prediction of relapse, hospitalization, and suicide in bipolar disorder using neuroimaging and clinical data: A systematic review.

BACKGROUND: Bipolar disorder (BD) is associated with increased morbidity/mortality. Adverse outcome prediction might help with the management of patients with BD.

Glycocalyx shedding patterns identifies antipsychotic-naïve patients with first-episode psychosis.

Psychotic disorders have been linked to immune-system abnormalities, increased inflammatory markers, and subtle neuroinflammation. Studies further sug...

Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasets.

Major depressive disorder (MDD) is the leading cause of disability worldwide, yet treatment selection still proceeds via "trial and error". Given the ...

A machine learning approach for differentiating bipolar disorder type II and borderline personality disorder using electroencephalography and cognitive abnormalities.

This study addresses the challenge of differentiating between bipolar disorder II (BD II) and borderline personality disorder (BPD), which is complica...

Individual Predictors of Response to A Behavioral Activation-Based Digital Smoking Cessation Intervention: A Machine Learning Approach.

Depression is prevalent among individuals who smoke cigarettes and increases risk for relapse. A previous clinical trial suggests that Goal2Quit, a b...

Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review.

OBJECTIVES: This study underscores the importance of exploring AI's creative applications in treating depressive disorders to revolutionize mental hea...

GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals.

Identifying major depressive disorder (MDD) using objective physiological signals has become a pressing challenge.Hence, this paper proposes a graph c...

Mining key circadian biomarkers for major depressive disorder by integrating bioinformatics and machine learning.

OBJECTIVE: This study aimed to identify key clock genes closely associated with major depressive disorder (MDD) using bioinformatics and machine learn...

Economic benefit analysis of lithium battery recycling based on machine learning algorithm.

Lithium batteries, as an important energy storage device, are widely used in the fields of renewable vehicles and renewable energy. The related lithiu...

Exploring the potential of representation and transfer learning for anatomical neuroimaging: Application to psychiatry.

The perspective of personalized medicine for brain disorders requires efficient learning models for anatomical neuroimaging-based prediction of clinic...

Gradient Matching Federated Domain Adaptation for Brain Image Classification.

Federated learning has shown its unique advantages in many different tasks, including brain image analysis. It provides a new way to train deep learni...

EEG based functional connectivity in resting and emotional states may identify major depressive disorder using machine learning.

OBJECTIVE: Disrupted brain network connectivity underlies major depressive disorder (MDD). Altered EEG based Functional connectivity (FC) with Emotion...

Graph convolutional network with attention mechanism improve major depressive depression diagnosis based on plasma biomarkers and neuroimaging data.

BACKGROUND: The absence of clinically-validated biomarkers or objective protocols hinders effective major depressive disorder (MDD) diagnosis. Compare...

Predicting treatment resistance in schizophrenia patients: Machine learning highlights the role of early pathophysiologic features.

Detecting patients with a high-risk profile for treatment-resistant schizophrenia (TRS) can be beneficial for implementing individually adapted therap...

A predictive study of the efficacy of transcutaneous auricular vagus nerve stimulation in the treatment of major depressive disorder: An fMRI-based machine learning analysis.

BACKGROUND: In order to improve taVNS efficacy, the usage of fMRI to explore the predictive neuroimaging markers would be beneficial for screening the...

A machine learning model to predict the risk of perinatal depression: Psychosocial and sleep-related factors in the Life-ON study cohort.

Perinatal depression (PND) is a common complication of pregnancy associated with serious health consequences for both mothers and their babies. Identi...

MicroRNA classification and discovery for major depressive disorder diagnosis: Towards a robust and interpretable machine learning approach.

BACKGROUND: Major depressive disorder (MDD) is notably underdiagnosed and undertreated due to its complex nature and subjective diagnostic methods. Bi...

A novel machine learning-based prediction method for patients at risk of developing depressive symptoms using a small data.

The prediction of depression is a crucial area of research which makes it one of the top priorities in mental health research as it enables early inte...

Identifying Cardiovascular Disease Risk Endotypes of Adolescent Major Depressive Disorder Using Exploratory Unsupervised Machine Learning.

OBJECTIVE: Adolescents with major depressive disorder (MDD) are at increased risk of premature atherosclerosis and cardiovascular disease (CVD). The a...