Psychiatry

Prospective prediction of first onset of major depressive disorder in midlife using machine learning.

PURPOSE: In this paper we leverage machine learning (ML) models to prospectively predict the first o...

Enhancing early detection and treatment of psychosis in Germany: a protocol for the health economic evaluation of an artificial intelligence-guided complex intervention.

INTRODUCTION: Psychosis, characterised by chronic symptoms often emerging in youth, imposes a substa...

Navigating the Maze of Social Media Disinformation on Psychiatric Illness and Charting Paths to Reliable Information for Mental Health Professionals: Observational Study of TikTok Videos.

BACKGROUND: Disinformation on social media can seriously affect mental health by spreading false inf...

Integrating AI and genomics: predictive CNN models for schizophrenia phenotypes.

This study explores the use of deep learning to analyze genetic data and predict phenotypic traits a...

Comprehensive framework for thyroid disorder diagnosis: Integrating advanced feature selection, genetic algorithms, and machine learning for enhanced accuracy and other performance matrices.

Thyroid hormones control crucial physiological activities, such as metabolism, oxidative stress, ery...

Differences in resting-state functional connectivity between depressed bipolar and major depressive disorder patients: A machine learning study.

Nearly 60 % of individuals with bipolar disorder (BD) are initially classified as major depressive d...

Comparative cytokine signatures and cognitive deficits in early-onset schizophrenia and adolescent major depression: Toward refined diagnostic classification frameworks.

BACKGROUND: This study analyzed plasma cytokine patterns in individuals with schizophrenia (SCZ), ma...

Impact of brain regions on attention deficit hyperactivity disorder (ADHD) electroencephalogram (EEG) signals: Comparison of machine learning algorithms with empirical mode decomposition and time domain analysis.

OBJECTIVE: This study emphasizes the importance of using proper combinations of brain area, extracti...

Predicting involuntary admission following inpatient psychiatric treatment using machine learning trained on electronic health record data - CORRIGENDUM.

Psychedelics, entactogens and psychoplastogens for depression and related disorders.

Currently, the most actively investigated rapidly acting antidepressants, anxiolytics and/or anti PT...

Machine learning identifies prominent risk factors for depressive symptoms among Chinese children and adolescents.

BACKGROUND: Identifying key risk factors for depressive symptoms in children and adolescents is cruc...

Psychosocial considerations in pediatric heart transplantation: Initial validation of the Pediatric Psychosocial Assessment Tool at a single center.

BACKGROUND: Pre-transplant psychosocial assessment, in conjunction with medical and surgical evaluat...

Redefining parameter-efficiency in ADHD diagnosis: A lightweight attention-driven kolmogorov-arnold network with reduced parameter complexity and a novel activation function.

As deep learning continues to advance in medical analysis, the increasing complexity of models, part...

From quality of life to sleep quality in Chinese college students: stress and anxiety as sequential mediators with nonlinear effects via machine learning.

OBJECT: This study examines how quality of life is associated with sleep quality among Chinese unive...

A case study of forensic psychiatry experts' reports analysis through large language models.

The integration of artificial intelligence (AI) technologies in forensic psychiatry has gained signi...

Neur-Ally: a deep learning model for regulatory variant prediction based on genomic and epigenomic features in brain and its validation in certain neurological disorders.

Large-scale quantitative studies have identified significant genetic associations for various neurol...

Role of large language models in mental health research: an international survey of researchers' practices and perspectives.

BACKGROUND: Large language models (LLMs) offer significant potential to streamline research workflow...

Air Pollution and Autism Spectrum Disorder: Unveiling Multipollutant Risks and Sociodemographic Influences in California.

BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental condition with increasing prevale...

Providing context: Extracting non-linear and dynamic temporal motifs from brain activity.

Approaches studying the dynamics of resting-state functional magnetic resonance imaging (rs-fMRI) ac...

Real-world effectiveness of cariprazine in major depressive disorder and bipolar I disorder in the United States.

AIMS: The efficacy of cariprazine for major depressive disorder (MDD) (adjunctive therapy) and bipol...

Detecting the clinical features of difficult-to-treat depression using synthetic data from large language models.

Difficult-to-treat depression (DTD) has been proposed as a broader and more clinically comprehensive...