Bipolar Disorder

Comprehensive Characterization of Antidepressant Pharmacogenetics: A Systematic Review of Studies in Major Depressive Disorder.

Pharmacogenetics is a promising strategy to facilitate individualized care for patients with Major Depressive Disorder (MDD). Research is ongoing to identify the optimal genetic markers for predicting outcomes to antidepressant therapies. The primary aim of this systematic review was to summarize antidepressant pharmacogenetic studies to enhance understanding of the genes, variants, datatypes/meth...

Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies.

BACKGROUND: Bipolar disorder (BD) is among the psychiatric disorders most prone to misdiagnosis, with both false positives and false negatives resulting in treatment delay. We employed a whole-brain machine learning approach focusing on gray matter volumes (GMVs) to contribute to defining objective biomarkers of BD and discriminating it from other forms of psychopathology, including subthreshold m...

HEDL: Deep learning multiple approaches for early detection of depression using sarcastic text.

Sarcasm is particularly notorious towards mental health, and thus it is quite essential for early identification of depressive indicators. This paper ...

Effect of Cumulative Exposure on the Efficacy of Paroxetine: A Population Pharmacokinetic-Pharmacodynamic and Machine Learning Analyses.

Selective serotonin reuptake inhibitors (SSRIs) are widely used in depression treatment. However, the relationship between treatment efficacy and plas...

Links between self-monitoring data collected through smartphones and smartwatches and the individual disease trajectories of adult patients with depressive disorders: Study protocol of a one-year observational trial.

Depression is highly recurrent and heterogenous in its individual course, requiring a personalized treatment approach. Patients today can collect larg...

Advances in EEG-based detection of Major Depressive Disorder using shallow and deep learning techniques: A systematic review.

The contemporary diagnosis of Major Depressive Disorder (MDD) primarily relies on subjective assessments and self-reported measures, often resulting i...

A novel framework for seasonal affective disorder detection: Comprehensive machine learning analysis using multimodal social media data and SMOTE.

Seasonal Affective Disorder (SAD) is a mood disorder characterized by recurring depressive episodes during specific seasons, particularly in Fall and ...

Using Machine Learning to Identify Predictors of Maternal and Infant Hair Cortisol Concentration Before and During the COVID-19 Pandemic.

Hair cortisol concentration (HCC) has been theorized to reflect chronic stress, and maternal and infant HCC may be correlated due to shared genetic, p...

Selection of AI model for predicting disability diseases through bipolar complex fuzzy linguistic multi-attribute decision-making technique based on operators.

The selection of suitable AI models to predict disability diseases stands as a vital multi-attribute decision-making (MADM) task within healthcare tec...

A novel artificial intelligence-based methodology to predict non-specific response to treatment.

Non-specific response to treatment (NSRT) is the primary contributor to the failure of randomized clinical trials in major depressive disorder (MDD). ...

Predictors of smartphone addiction in adolescents with depression: combing the machine learning and moderated mediation model approach.

Smartphone addiction (SA) significantly impacts the physical and mental health of adolescents, and can further exacerbate existing mental health issue...

Assessing bias in AI-driven psychiatric recommendations: A comparative cross-sectional study of chatbot-classified and CANMAT 2023 guideline for adjunctive therapy in difficult-to-treat depression.

The integration of chatbots into psychiatry introduces a novel approach to support clinical decision-making, but biases in their recommendations pose ...

Unsupervised Evolutionary Cell Type Matching via Entropy-Minimized Optimal Transport

Identifying evolutionary correspondences between cell types across species is a fundamental challenge in comparative genomics and evolutionary biolo...

Comparative Efficacy of MultiModal AI Methods in Screening for Major Depressive Disorder: Machine Learning Model Development Predictive Pilot Study.

BACKGROUND: Conventional approaches for major depressive disorder (MDD) screening rely on two effective but subjective paradigms: self-rated scales an...

3DGEER: Exact and Efficient Volumetric Rendering with 3D Gaussians

3D Gaussian Splatting (3DGS) marks a significant milestone in balancing the quality and efficiency of differentiable rendering. However, its high ef...

Accurate Prediction of Open-Circuit Voltages of Lithium-Ion Batteries via Delta Learning.

Accurate prediction of lithium-ion battery capacity before material synthesis is crucial for accelerating battery material discovery. The capacity can...

Activation of the Carotid Body by Kappa Opioid Receptors Mitigates Fentanyl-Induced Respiratory Depression.

Previous studies reported that opioids depress breathing by inhibiting respiratory neural networks in the brainstem. The effects of opioids on sensory...

Predicting Placebo Responses Using EEG and Deep Convolutional Neural Networks: Correlations with Clinical Data Across Three Independent Datasets.

Identifying likely placebo responders can help design more efficient clinical trials by stratifying participants, reducing sample size requirements, a...

Comparing interpretable machine learning models for fall risk in middle-aged and older adults with and without pain.

Pain is common in middle-aged and older adults, has also been identified as a fall risk factor, whereas the mechanism of falls in pain is unclear. Thi...

Machine-Learning-Guided Screening of Advantageous Solvents for Solid Polymer Electrolytes in Lithium Metal Batteries.

Trace residual solvents in solid polymer electrolytes (SPEs) significantly affect electrolyte and interface properties, where optimal selection enhanc...