Bipolar Disorder

Association of depressive symptoms with systemic immune-inflammation index and platelet parameters among survivors of myocardial infarction: a cross-sectional NHANES study enhanced by machine learning and SHAP analysis.

BACKGROUND: Inflammation is implicated in the elevated risk of depressive disorder following myocardial infarction (MI), with platelets serving as a key link between thrombosis, inflammation, and depression. Although the systemic immune-inflammation index (SII), platelet count (PLT), and mean platelet volume (MPV) are readily accessible hematological parameters, their associations with post-MI dep...

Uncertainty aware hybrid learning framework for fast and safe charging of lithium-ion batteries using multi-fidelity observers.

Accurate and real-time estimation of the state of charge (SoC) is critical for ensuring the performance, safety, and longevity of lithium-ion batteries in electric vehicles (EVs). This research work presents an Uncertainty-Aware Hybrid Learning Framework for accurate and safe State-of-Charge (SoC) prediction in lithium-ion batteries, particularly under dynamic and thermal-varying conditions during...

Dual-representation structural MRI classification of psychiatric disorders using deep learning and large language models.

Accurate differentiation among psychiatric disorders such as schizophrenia and bipolar disorder remains a significant clinical challenge due to overla...

The Retinal Age Gap as a Marker of Accelerated Aging in the Early Course of Schizophrenia.

BACKGROUND AND HYPOTHESIS: Given the available findings confirming accelerated brain aging in schizophrenia (SZ), we conducted a study aimed at verify...

Deep learning for detecting depression in individuals with and without alexithymia.

BACKGROUND: To accurately detect individuals' mental health issues using artificial intelligence and self-report scales, it is crucial to recognize ho...

Machine learning guided structural dynamics identifies translation elongation factor 1 (EEF1A1) as an immunological biomarker and marine natural products as therapeutic leads for rheumatoid arthritis with major depressive disorder.

Rheumatoid arthritis (RA) is a systemic autoimmune disease that predominantly affects synovial joints, especially those of the hands, elbows, wrists, ...

Randomized evidence on AI-enabled personalized treatment planning for adults with depressive-anxiety Spectrum disorders: A systematic review and Meta-analysis.

BACKGROUND: AI-enabled personalized treatment planning may improve outcomes by tailoring care, yet its clinical impact across modalities remains uncer...

Rumination is more important than other variables in the association between social media use and depression: A multi-method study.

The relationship between social media use (SMU) and mental health, particularly depression, remains inconclusive. The present study recruited 925 coll...

Neural signatures of major depressive disorder with comorbid insomnia: Low gamma PoCG.R-INS.L connectivity as a potential biomarker.

BACKGROUND: Insomnia is a common and distressing symptom in major depressive disorder (MDD). However, research focusing on the neurobiological mechani...

P300-based support vector machine model for identifying adolescent with first-episode, drug-naïve major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is diagnosed mainly through clinical interviews, highlighting a need for objective neurophysiological meas...

Identification of major depressive disorder based on Triple-GCN model constructed with multimodal elastic network from higher-order brain connectivity.

Major Depressive Disorder (MDD) is the common mental health disease threatening human well-being. Several neuroimaging studies show that analyzing neu...

Gaborformer: A method for depression detection through hybrid acoustic feature extraction and fusion.

Depression, a complex and heterogeneous mental disorder, poses significant challenges for timely and effective detection, highlighting the need for ad...

Advancing patient stratification in major depressive disorder: Evaluation of clinical staging and machine learning prediction models based on real-world data.

OBJECTIVE: To evaluate the utility of a clinical staging model and compared its prognostic performance with an unsupervised machine learning-based str...

Depression detection from speech data using deep learning-based optimized temporal-frequency-channel attention with interpretable acoustic-prosodic mapping.

Detecting depression from voice recordings is challenging because acoustic indicators are often highly subtle and vary broadly among individuals. A ke...

Exploring temperamental and clinical predictors of lithium treatment outcomes in bipolar disorder using diverse machine learning approaches.

BACKGROUND: Lithium is a core treatment for bipolar disorder (BD), yet clinical response varies across patients. Testing accessible predictors of lith...

Interpretable Aging Signatures in Human Retinal Cell Types Revealed by Single-Cell RNA Sequencing and Sparse Logistic Regression.

PURPOSE: To characterize cell-type-specific transcriptional changes during human retinal aging and develop machine learning (ML) model for cellular ag...

Multi-scale EEG analysis identifies neural circuit signatures of iTBS responsiveness in major depressive disorder.

BACKGROUND: Response to transcranial magnetic stimulation (TMS) in major depressive disorder (MDD) is highly variable, underscoring the need for bioma...

Is chronic low back pain related to sleep disturbances? A self-organizing maps for unsupervised machine learning-based patient phenotyping.

BACKGROUND CONTEXT: Chronic low back pain (CLBP) is a multifactorial condition and a leading cause of disability worldwide. Among the various contribu...

Dataset of ultrasonic frequency - domain signals and machine - learning outputs for parameterising lithium - ion battery electrodes' coating and calendering processes.

Inline, non - destructive diagnostics are essential for controlling electrode quality in roll - to - roll battery manufacturing to minimise the cost a...

The role of AI-driven communication in delirium prevention, detection, and care for critically ill ICU patients: A systematic review with inductive thematic synthesis.

BACKGROUND: Delirium remains one of the most consequential complications among critically ill patients in ICUs, exerting profound effects on morbidity...