Psychiatry

Visualizing functional network connectivity differences using an explainable machine-learning method.

. Functional network connectivity (FNC) estimated from resting-state functional magnetic resonance imaging showed great information about the neural mechanism in different brain disorders. But previous research has mainly focused on standard statistical learning approaches to find FNC features separating patients from control. While machine learning models can improve classification accuracy, they...

A depression detection approach leveraging transfer learning with single-channel EEG.

Major depressive disorder (MDD) is a widespread mental disorder that affects health. Many methods combining electroencephalography (EEG) with machine learning or deep learning have been proposed to objectively distinguish between MDD and healthy individuals. However, most current methods detect depression based on multichannel EEG signals, which constrains its application in daily life. The contex...

Predictors of psychotic experiences among adolescents with obsessive-compulsive symptoms: A data-driven machine learning approach.

AIM: Prediction of future psychosis in individuals with obsessive and compulsive (OC) symptoms is crucial for treatment choice, but only a few predict...

The Relationship Between Anxiety and Readiness Levels Regarding Artificial Intelligence in Midwives: An Intergenerational Comparative Study.

This study aimed to compare Generations X, Y, and Z in terms of anxiety and readiness levels regarding artificial intelligence and investigate the rel...

Predicting the onset of mental health problems in adolescents.

OBJECTIVE: Mental health problems are the major cause of disability among adolescents. Personalized prevention may help to mitigate the development of...

Harnessing an Artificial Intelligence-Based Large Language Model With Personal Health Record Capability for Personalized Information Support in Postsurgery Myocardial Infarction: Descriptive Qualitative Study.

BACKGROUND: Myocardial infarction (MI) remains a leading cause of morbidity and mortality worldwide. Although postsurgical cardiac interventions have ...

Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health: Explainable Artificial Intelligence Approach.

BACKGROUND: Perinatal depression and anxiety significantly impact maternal and infant health, potentially leading to severe outcomes like preterm birt...

Mediating effect of AI attitudes and AI literacy on the relationship between career self-efficacy and job-seeking anxiety.

As artificial intelligence (AI) technology quickly grows, college students have new worries and fears. Using Marx's theory of labour alienation, this ...

A scoping review of large language models for generative tasks in mental health care.

Large language models (LLMs) show promise in mental health care for handling human-like conversations, but their effectiveness remains uncertain. This...

Health-Promoting Effects and Everyday Experiences With a Mental Health App Using Ecological Momentary Assessments and AI-Based Ecological Momentary Interventions Among Young People: Qualitative Interview and Focus Group Study.

BACKGROUND: Considering the high prevalence of mental health conditions among young people and the technological advancements of artificial intelligen...

Topology-Guided Graph Masked Autoencoder Learning for Population-Based Neurodevelopmental Disorder Diagnosis.

Exploring the pathogenic mechanisms of brain disorders within population is an important research in the field of neuroscience. Existing methods eithe...

Suicide risk prediction for Korean adolescents based on machine learning.

Traditional clinical risk assessment tools proved inadequate for reliably identifying individuals at high risk for suicidal behavior. As a result, mac...

Biological age prediction in schizophrenia using brain MRI, gut microbiome and blood data.

The study of biological age prediction using various biological data has been widely explored. However, single biological data may offer limited insig...

The role of artificial intelligence in the prediction, identification, diagnosis and treatment of perinatal depression and anxiety among women in LMICs: a systematic review protocol.

INTRODUCTION: Perinatal depression and anxiety (PDA) is associated with a high risk of maternal mortality. Existing data shows that 95% of maternal mo...

Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression.

Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (...

Brain circuits that regulate social behavior.

Social interactions are essential for the survival of individuals and the reproduction of populations. Social stressors, such as social defeat and iso...

Using machine learning approach to predict suicide ideation and suicide attempts among Chinese adolescents: A cross-sectional study.

BACKGROUND: Screening for suicide ideation and suicide attempts is crucial for adolescents, yet accurately predicting these outcomes remains a signifi...

Therapeutic potential of enzymes, neurosteroids, and synthetic steroids in neurodegenerative disorders: A critical review.

Neurodegenerative disorders present a significant challenge to healthcare systems, mainly due to the limited availability of effective treatment optio...

Expert and Interdisciplinary Analysis of AI-Driven Chatbots for Mental Health Support: Mixed Methods Study.

BACKGROUND: Recent years have seen an immense surge in the creation and use of chatbots as social and mental health companions. Aiming to provide empa...

Trade-offs between machine learning and deep learning for mental illness detection on social media.

Social media platforms provide valuable insights into mental health trends by capturing user-generated discussions on conditions such as depression, a...