Psychiatry

Exploring the triglyceride-glucose index's role in depression and cognitive dysfunction: Evidence from NHANES with machine learning support.

BACKGROUND: Depression and cognitive impairments are prevalent among older adults, with evidence suggesting potential links to obesity and lipid metabolism disturbances. This study investigates the relationships between the triglyceride-glucose (TyG) index, body mass index (BMI), depression, and cognitive dysfunction in older adults, leveraging data from the NHANES survey and employing machine lea...

Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder.

Depression treatment responses vary widely among individuals. Identifying objective biomarkers with predictive accuracy for therapeutic outcomes can enhance treatment efficiency and avoid ineffective therapies. This study investigates whether functional near-infrared spectroscopy (fNIRS) and clinical assessment information can predict treatment response in major depressive disorder (MDD) through m...

Application of machine learning in depression risk prediction for connective tissue diseases.

This study retrospectively collected clinical data from 480 patients with connective tissue diseases (CTDs) at Nanjing First Hospital between August 2...

Is Artificial Intelligence the Next Co-Pilot for Primary Care in Diagnosing and Recommending Treatments for Depression?

Depression poses significant challenges to global healthcare systems and impacts the quality of life of individuals and their family members. Recent a...

Multi-center brain age prediction via dual-modality fusion convolutional network.

Accurate prediction of brain age is crucial for identifying deviations between typical individual brain development trajectories and neuropsychiatric ...

Musicians and researchers: two creative professions striving to improve heart health through music.

Musicians and researchers are creative professions that share many similarities. They both aim to bring joy and progress to humanity. In recent decade...

Partial directed coherence analysis of resting-state EEG signals for alcohol use disorder detection using machine learning.

INTRODUCTION: Excessive alcohol consumption negatively impacts physical and psychiatric health, lifestyle, and societal interactions. Chronic alcohol ...

A novel muscle network approach for objective assessment and profiling of bulbar involvement in ALS.

INTRODUCTION: As a hallmark feature of amyotrophic lateral sclerosis (ALS), bulbar involvement significantly impacts psychosocial, emotional, and phys...

Depression-related innate immune genes and pan-cancer gene analysis and validation.

BACKGROUND: Depression, a prevalent chronic mental disorder, presents complexities and treatment challenges that drive researchers to seek new, precis...

Brain mapping, biomarker identification and using machine learning method for diagnosis of anxiety during emotional face in preschool children.

BACKGROUND: Due to the importance and the consequences of anxiety, the goals of the current study are brain mapping, biomarker identification and the ...

Enhancing prediction of major depressive disorder onset in adolescents: A machine learning approach.

Major Depressive Disorder (MDD) is a prevalent mental health condition that often begins in adolescence, with significant long-term implications. Indi...

Machine-learning-based cost prediction models for inpatients with mental disorders in China.

BACKGROUND: Mental disorders are increasingly prevalent, leading to increased medical expenditures. To refine the reimbursement of medical costs for i...

Automated classification of stress and relaxation responses in major depressive disorder, panic disorder, and healthy participants via heart rate variability.

BACKGROUND: Stress is a significant risk factor for psychiatric disorders such as major depressive disorder (MDD) and panic disorder (PD). This highli...

Prediction of health anxiety using resting-state functional near-infrared spectroscopy and machine learning.

BACKGROUND: The role of cortical networks in health anxiety remain poorly understood. This study aimed to develop a predictive model for health anxiet...

Predicting cardiovascular disease in patients with mental illness using machine learning.

BACKGROUND: Cardiovascular disease (CVD) is twice as prevalent among individuals with mental illness compared to the general population. Prevention st...

Schizophrenia more employable than depression? Language-based artificial intelligence model ratings for employability of psychiatric diagnoses and somatic and healthy controls.

Artificial Intelligence (AI) assists recruiting and job searching. Such systems can be biased against certain characteristics. This results in potenti...

The Goldilocks Zone: Finding the right balance of user and institutional risk for suicide-related generative AI queries.

Generative artificial intelligence (genAI) has potential to improve healthcare by reducing clinician burden and expanding services, among other uses. ...

Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning.

Spontaneous blood oxygen level-dependent signals can be indirectly recorded in different brain regions with functional magnetic resonance imaging. In ...

A Plasma Proteomics-Based Model for Identifying the Risk of Postpartum Depression Using Machine Learning.

Postpartum depression (PPD) poses significant risks to maternal and infant health, yet proteomic analyses of PPD-risk women remain limited. This study...

MO-GCN: A multi-omics graph convolutional network for discriminative analysis of schizophrenia.

The methodology of machine learning with multi-omics data has been widely adopted in the discriminative analyses of schizophrenia, but most of these s...