Psychiatry

Evaluation of an AI-Based Voice Biomarker Tool to Detect Signals Consistent With Moderate to Severe Depression.

PURPOSE: Mental health screening is recommended by the US Preventive Services Task Force for all patients in areas where treatment options are available. Still, it is estimated that only 4% of primary care patients are screened for depression. The goal of this study was to evaluate the efficacy of machine learning technology (Kintsugi Voice, v1, Kintsugi Mindful Wellness, Inc) to detect and analyz...

The influence of generative artificial intelligence on creative cognition of design students: a chain mediation model of self-efficacy and anxiety.

INTRODUCTION: This study investigated the role of generative Artificial Intelligence (AI) in enhancing the creative cognition of design students, examining the mediating effects of self-efficacy and anxiety reduction.

Identifying autism spectrum disorder based on machine learning for multi-site fMRI.

BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by repetitive stereotypical behavior and social impairment. ...

Development of a machine learning-based multivariable prediction model for the naturalistic course of generalized anxiety disorder.

BACKGROUND: Generalized Anxiety Disorder (GAD) is a chronic condition. Enabling the prediction of individual trajectories would facilitate tailored ma...

A machine learning model the prediction of athlete engagement based on cohesion, passion and mental toughness.

Athlete engagement is influenced by several factors, including cohesion, passion and mental toughness. Machine learning methods are frequently employe...

Detecting noncredible symptomology in ADHD evaluations using machine learning.

INTRODUCTION: Diagnostic evaluations for attention-deficit/hyperactivity disorder (ADHD) are becoming increasingly complicated by the number of adults...

The Use of AI in Mental Health Services to Support Decision-Making: Scoping Review.

BACKGROUND: Recent advancements in artificial intelligence (AI) have changed the care processes in mental health, particularly in decision-making supp...

Explainable machine learning model for assessing health status in patients with comorbid coronary heart disease and depression: Development and validation study.

BACKGROUND: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health s...

Assessment of glymphatic function and white matter integrity in children with autism using multi-parametric MRI and machine learning.

OBJECTIVES: To assess glymphatic function and white matter integrity in children with autism spectrum disorder (ASD) using multi-parametric MRI, combi...

Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review.

BACKGROUND: Prevention of suicide is a global health priority. Approximately 800,000 individuals die by suicide yearly, and for every suicide death, t...

Deep learning based prediction of depression and anxiety in patients with type 2 diabetes mellitus using regional electronic health records.

BACKGROUND: Depression and anxiety are prevalent mental health conditions among individuals with type 2 diabetes mellitus (T2DM), who exhibit unique v...

The integrating of environmental sustainability assessment by using bipolar complex fuzzy soft Aczel-Alsina aggregation operators with EDAS approach.

The act of responsibly engaging with the world is referred to as environmental sustainability. It entails preserving the world's ecosystems and natura...

A comparative machine learning study of schizophrenia biomarkers derived from functional connectivity.

Functional connectivity holds promise as a biomarker of schizophrenia. Yet, the high dimensionality of predictive models trained on functional connect...

Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis.

: Recent research has focused on exploring the relationships between various factors associated with headaches and understanding their impact on indiv...

Identifying Neuro-Inflammatory Biomarkers of Generalized Anxiety Disorder from Lymphocyte Subsets Based on Machine Learning Approaches.

INTRODUCTION: Activation of the inflammatory response system is involved in the pathogenesis of generalized anxiety disorder (GAD). The purpose of thi...

Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach.

Anomalous chromosomes are the cause of genetic diseases such as cancer, Alzheimer's, Parkinson's, epilepsy, and autism. Karyotype analysis is the stan...

Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized care.

Autism Spectrum Disorder (ASD) affects millions of individuals worldwide, presenting challenges in social communication, repetitive behaviors, and sen...

Development and application of a machine learning-based antenatal depression prediction model.

BACKGROUND: Antenatal depression (AND), occurring during pregnancy, is associated with severe outcomes. However, there is a lack of objective and univ...

From social media to artificial intelligence: improving research on digital harms in youth.

In this Personal View, we critically evaluate the limitations and underlying challenges of existing research into the negative mental health consequen...

Guardian-BERT: Early detection of self-injury and suicidal signs with language technologies in electronic health reports.

Mental health disorders, including non-suicidal self-injury (NSSI) and suicidal behavior, represent a growing global concern. Early detection of these...