Psychiatry

Bipolar Disorder

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

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Showing 43-63 of 830 articles
Integrating Molecular Dynamics and Machine Learning for Solvation-Guided Electrolyte Optimization in Lithium Metal Batteries.

Optimizing liquid electrolytes is essential for achieving long-term cycling stability and high safet...

De novo design and bioactivity prediction of mitotic kinesin Eg5 inhibitors using MPNN and LSTM-based transfer learning.

Breast cancer, the most commonly diagnosed disease worldwide, has been linked to the overexpression ...

Alterations in Gut Microbiota-Brain Axis in Major Depressive Disorder as Identified by Machine Learning.

Major depressive disorder (MDD) is a complex mental health condition whose causes may extend beyond ...

Leveraging machine learning to uncover the hidden links between trusting behavior and biological markers.

Understanding the decision-making mechanisms underlying trust is essential, particularly for individ...

Functional connectivity alterations of the pregenual anterior cingulate cortex by ketamine and the modulation by lamotrigine.

BACKGROUND: Neuroimaging studies have linked the beneficial effects of subanaesthetic ketamine doses...

Multitarget Generate Electrolyte Additive for Lithium Metal Batteries.

Electrolyte additives are crucial for accelerating the commercialization of lithium metal batteries ...

Smartphone eye-tracking with deep learning: Data quality and field testing.

Eye-tracking is widely used to measure human attention in research, commercial, and clinical applica...

Prospective prediction of first onset of major depressive disorder in midlife using machine learning.

PURPOSE: In this paper we leverage machine learning (ML) models to prospectively predict the first o...

Differences in resting-state functional connectivity between depressed bipolar and major depressive disorder patients: A machine learning study.

Nearly 60 % of individuals with bipolar disorder (BD) are initially classified as major depressive d...

Psychedelics, entactogens and psychoplastogens for depression and related disorders.

Currently, the most actively investigated rapidly acting antidepressants, anxiolytics and/or anti PT...

Machine learning identifies prominent risk factors for depressive symptoms among Chinese children and adolescents.

BACKGROUND: Identifying key risk factors for depressive symptoms in children and adolescents is cruc...

Transfer learning-motivated intelligent fault diagnosis framework for cross-domain knowledge distillation.

Transfer learning, as a transformative learning paradigm, has revolutionized the application of arti...

Real-world effectiveness of cariprazine in major depressive disorder and bipolar I disorder in the United States.

AIMS: The efficacy of cariprazine for major depressive disorder (MDD) (adjunctive therapy) and bipol...

Detecting the clinical features of difficult-to-treat depression using synthetic data from large language models.

Difficult-to-treat depression (DTD) has been proposed as a broader and more clinically comprehensive...

Enhancing differentiation between unipolar and bipolar depression through integration of machine learning and electroencephalogram analysis.

To enhance the differentiation between unipolar depression (UPD) and bipolar depression (BPD), this ...

Multivariate brain morphological patterns across mood disorders: key roles of frontotemporal and cerebellar areas.

BACKGROUND: Differentiating major depressive disorder (MDD) from bipolar disorder (BD) remains a sig...

Machine learning for classification of pediatric bipolar disorder with and without psychotic symptoms based on thalamic subregional structural volume.

BACKGROUND: The thalamus plays a crucial role in sensory processing, emotional regulation, and cogni...

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