Psychiatry

It is Time to Realize the Promise of the Digital Mental Health Transformation: Application for Population Mental Health.

The past 25 years have seen the explosion of digital health care-from 1s and 0s initially serving most researchers for accomplishing their work, to the creation of smartphones, mHealth, and more recently artificial intelligence. The revolution for digital mental health is no longer in its infancy, as new tools are created to address mental health, sometimes even undergoing evaluation for adoption ...

Causal AI Recommendation System for Digital Mental Health: Bayesian Decision-Theoretic Analysis.

BACKGROUND: Digital mental health tools promise to enhance the reach and quality of care. Current tools often recommend content to individuals, typically using generic knowledge-based systems or predictive artificial intelligence (AI). However, predictive AI is problematic for interventional recommendations as cause-effect relationships can be confounded in observed data. Therefore, causal AI is r...

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 associated with schizophrenia, a complex psychiatri...

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 information, increasing anxiety, stress, and confusio...

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 onset of Major Depressive Disorder (MDD), one of th...

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 substantial burden on individuals and healthcare systems...

Smartphone eye-tracking with deep learning: Data quality and field testing.

Eye-tracking is widely used to measure human attention in research, commercial, and clinical applications. With the rapid advancements in artificial i...

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, erythropoiesis, thermoregulation, and organ developme...

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

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, extraction of features, and machine learning (ML) techniqu...

Analysis of the neural mechanisms of social anxiety based on EEG features and machine learning and construction of a diagnostic model.

Social anxiety is a common psychological problem, and its accurate diagnosis and investigation of underlying neurophysiological mechanisms are of sign...

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), major depressive disorder (MDD), and healthy control...

Psychedelics, entactogens and psychoplastogens for depression and related disorders.

Currently, the most actively investigated rapidly acting antidepressants, anxiolytics and/or anti PTSD agents, include psychedelics e.g. psilocybin, L...

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 crucial for prevention. However, few studies have expl...

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 significant attention due to their potential to enhance...

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 university students through sequential mediation by per...

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 neurological disorders. Expression quantitative trait lo...

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 workflows and enhance productivity. However, limited data ...

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 prevalence worldwide. Air pollution may be a major contri...

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) activity often focus on time-resolved functional con...