Psychiatry

Bipolar Disorder

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

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Showing 22-42 of 830 articles
Machine learning based prediction of MnO cathode discharge capacity for high-performance zinc-ion batteries.

Zinc-ion batteries (ZIBs) are considered as a cheaper, non-toxic and safer alternative to lithium-io...

Leveraging stacked classifiers for exploring the role of hedonic processing between major depressive disorder and schizophrenia.

BACKGROUND: Anhedonia, a transdiagnostic feature common to both Major Depressive Disorder (MDD) and ...

A randomised cross over trial examining the linguistic markers of depression and anxiety in symptomatic adults.

Linguistic features within individuals' text data may indicate their mental health. This trial exami...

Predicting clozapine-induced adverse drug reaction biomarkers using machine learning.

Clozapine is an atypical antipsychotic used for patients with treatment-resistant schizophrenia. Thi...

Advancing Early Detection of Major Depressive Disorder Using Multisite Functional Magnetic Resonance Imaging Data: Comparative Analysis of AI Models.

BACKGROUND: Major depressive disorder (MDD) is a highly prevalent mental health condition with signi...

Comparative effectiveness of anti-seizure medications in emulated trials using medical informatics.

Anti-seizure medications (ASMs) are often prescribed using a trial-and-error approach with a similar...

Mimicking the Peptidyl Enzyme Enables Polysulfide Electronic Axial Stretching Catalysis for Lean-Electrolyte Lithium-Sulfur Batteries.

Catalysts are effective in mitigating slow sulfur redox reaction (SRR) kinetics in lithium-sulfur (L...

Uncertainty aware domain incremental learning for cross domain depression detection.

Deep learning techniques have demonstrated significant promise for detecting Major Depressive Disord...

Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech.

Psychosis poses substantial social and healthcare burdens. The analysis of speech is a promising app...

A Generalizable Machine Learning Framework for Identifying Sustainable Multi-Ion Garnet Electrolytes.

Lithium-ion (Li-ion) solid-state batteries (SSBs) are highly regarded for their exceptional energy d...

Assessment of suicidal risk factors in young depressed persons with non-suicidal self-injury based on an artificial intelligence.

INTRODUCTION: The role of non-suicidal self-injury (NSSI) in the suicide process of people with majo...

Battery management in IoT hybrid grid system using deep learning algorithms based on crowd sensing and micro climatic data.

Hybrid Grid System (HGS) installation in small and large residential area has major challenges due t...

Comparing traditional natural language processing and large language models for mental health status classification: a multi-model evaluation.

The substantial increase in mental health disorders globally necessitates scalable, accurate tools f...

Characteristics of brain network connectome and connectome-based efficacy predictive model in bipolar depression.

Aberrant functional connectivity (FC) between brain networks has been indicated closely associated w...

Can circadian rhythms of heart rate variability identify major depressive disorder? - A study based on support vector machine analysis.

BACKGROUND: Major depressive disorder (MDD) is a prevalent and severe psychiatric condition for whic...

Using Machine Learning to Predict Treatment Outcome in a Concatenated Dataset of Youth Anxiety Treatments.

Machine Learning (ML) is a promising approach for predicting outcomes of youth anxiety treatments. T...

Chemical Reactions in Molten Lithium Carbonates and Hydroxides with Deep Potential Molecular Dynamics.

We generated a machine learning potential model to study chemical reactions in the liquid phase and ...

Machine learning-based model for behavioural analysis in rodents applied to the forced swim test.

The Forced Swim Test (FST) is a widely used preclinical model for assessing antidepressant efficacy,...

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