Psychiatry

EEG microstate analysis and machine learning classification in patients with obsessive-compulsive disorder.

BACKGROUND: Microstate characterization of electroencephalogram (EEG) is a data-driven approach to explore the functional changes and interrelationships of multiple brain networks on a millisecond scale. This study aimed to explore the pathological changes of whole-brain functional networks in patients with obsessive-compulsive disorders (OCD) through microstate analysis and further to explore its...

Prediction of late-onset depression in the elderly Korean population using machine learning algorithms.

Late-onset depression (LOD) refers to depression that newly appears in elderly individuals without prior depression episodes. Predicting future depression is crucial for mitigating the risk of major depression in prospective patients. This study aims to develop machine learning models to predict future depression. Using public data from the nationwide panel survey 'Korean Longitudinal Study of Agi...

A Trustworthy Curriculum Learning Guided Multi-Target Domain Adaptation Network for Autism Spectrum Disorder Classification.

Domain adaptation has demonstrated success in classification of multi-center autism spectrum disorder (ASD). However, current domain adaptation method...

Applied pharmacogenetics to predict response to treatment of first psychotic episode: study protocol.

The application of personalized medicine in patients with first-episode psychosis (FEP) requires tools for classifying patients according to their res...

Key risk factors of generalized anxiety disorder in adolescents: machine learning study.

Adolescents worldwide are increasingly affected by mental health disorders, with anxiety disorders, including Generalized Anxiety Disorder (GAD), bein...

Machine learning algorithms to predict depression in older adults in China: a cross-sectional study.

OBJECTIVE: The 2-fold objective of this research is to investigate machine learning's (ML) predictive value for the incidence of depression among Chin...

Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions.

INTRODUCTION: Machine learning (ML) is an effective tool for predicting mental states and is a key technology in digital psychiatry. This study aimed ...

Early detection of autism spectrum disorder: gait deviations and machine learning.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder diagnosed by clinicians and experts through questionnaires, observations, and intervie...

Beyond the hot flashes: how machine learning is uncovering the complexity of menopause-related depression.

BACKGROUND: The transition into menopause marks a significant stage in a woman's life, indicating the end of reproductive capability. This period, enc...

Neural correlates of facial recognition deficits in autism spectrum disorder: a comprehensive review.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by significant impairments in social interaction, often manifested in f...

DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI.

In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusion MRI, namely DDEvENet, for anatomical brain parc...

What is the influence of psychosocial factors on artificial intelligence appropriation in college students?

BACKGROUND: In recent years, the adoption of artificial intelligence (AI) has become increasingly relevant in various sectors, including higher educat...

A Longitudinal Prediction of Suicide Attempts in Borderline Personality Disorder: A Machine Learning Study.

Borderline personality disorder (BPD) is associated with a high risk of suicide. Despite several risk factors being known, identifying vulnerable pati...

Personalized stress optimization intervention to reduce adolescents' anxiety: A randomized controlled trial leveraging machine learning.

Anxiety symptoms are among the most prevalent mental health disorders in adolescents, highlighting the need for scalable and accessible interventions....

Developing a simplified measure to predict the risk of autism spectrum disorders: Abbreviating the M-CHAT-R using a machine learning approach in China.

BACKGROUND: Early screening for autism spectrum disorder (ASD) is crucial, yet current assessment tools in Chinese primary child care are limited in e...

Differential gray matter correlates and machine learning prediction of abuse and internalizing psychopathology in adolescent females.

Childhood abuse represents one of the most potent risk factors for the development of psychopathology during childhood, accounting for 30-60% of the r...

Psychotropic medications: a descriptive study of prescription trends in Tabriz, Iran, 2021-2022.

INTRODUCTION: Mental disorders, such as anxiety and depression, significantly impacted global populations in 2019 and 2020, with COVID-19 causing a su...

Leveraging deep learning for robust EEG analysis in mental health monitoring.

INTRODUCTION: Mental health monitoring utilizing EEG analysis has garnered notable interest due to the non-invasive characteristics and rich temporal ...

How do neighborhood environments impact adolescent health: a comprehensive study from subjective and objective perspectives using machine learning method.

Existing studies have established a linear relationship between urban environments and adolescent health, but the combined impacts of subjective and o...

Why robot embodiment matters: questions of disability, race and intersectionality in the design of social robots.

A growing minority of those with disabilities are people of color (POC), with, for example, autism diagnosis rates now higher for children of color th...