Psychiatry

Identifying Preliminary Risk Profiles for Dissociation in 16- to 25-Year-Olds Using Machine Learning.

INTRODUCTION: Dissociation is associated with clinical severity, increased risk of suicide and self-...

Identification of Depression Subtypes in Parkinson's Disease Patients via Structural MRI Whole-Brain Radiomics: An Unsupervised Machine Learning Study.

OBJECTIVE: Current clinical evaluation may tend to lack precision in detecting depression in Parkins...

Association of machine-learning-rated supportive counseling skills with psychotherapy outcome.

OBJECTIVE: This study applied a machine-learning-based skill assessment system to investigate the as...

Computing 3-Step Theory of Suicide Factor Scores From Veterans Health Administration Clinical Progress Notes.

BACKGROUND: Literature on how to translate information extracted from clinical progress notes into n...

The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review.

Artificial intelligence (AI) has been increasingly used in delivering mental healthcare worldwide. W...

The Effect of Nursing Students' Artificial Intelligence Anxiety on Their Knowledge of Robotic Surgery: The Mediating Role of Individual Innovativeness.

AIMS: This study aims of determine the mediating role of individual innovativeness in the effect of ...

A psychologically interpretable artificial intelligence framework for the screening of loneliness, depression, and anxiety.

Negative emotions such as loneliness, depression, and anxiety (LDA) are prevalent and pose significa...

The benefits of physical literacy for human flourishing: A machine learning-based exploration of adolescents.

Physical literacy is a multidimensional concept considered fundamental for lifelong participation in...

Large language models in oncology: a review.

Large language models (LLMs) have demonstrated emergent human-like capabilities in natural language ...

Acceptance of Using Artificial Intelligence and Digital Technology for Mental Health Interventions: The Development and Initial Validation of the UTAUT-AI-DMHI.

Digital health technologies are being increasingly integrated into mental healthcare. This means tha...

Personalization variables in digital mental health interventions for depression and anxiety in adolescents and youth: a scoping review.

INTRODUCTION: The impact of personalization on user engagement and adherence in digital mental healt...

Acoustic-based machine learning approaches for depression detection in Chinese university students.

BACKGROUND: Depression is major global public health problems among university students. Currently, ...

Gray and white matter alterations in Obsessive-Compulsive Personality Disorder: a data fusion machine learning approach.

INTRODUCTION: Obsessive-Compulsive Personality Disorder (OCPD) is a complex mental condition marked ...

Assessing ML classification algorithms and NLP techniques for depression detection: An experimental case study.

CONTEXT AND BACKGROUND: Depression has affected millions of people worldwide and has become one of t...

Artificial intelligence for contextual well-being: Protocol for an exploratory sequential mixed methods study with medical students as a social microcosm.

INTRODUCTION: AI-powered conversational agents have proven effective in alleviating psychological di...

Editorial: Machine learning and statistical models: unraveling patterns and enhancing understanding of mental disorders.

From the -Factor to Cognitive Content: Detection and Discrimination of Psychopathologies Based on Explainable Artificial Intelligence.

Differentiating psychopathologies is challenging due to shared underlying mechanisms, such as the -...

Employing large language models for emotion detection in psychotherapy transcripts.

PURPOSE: In the context of psychotherapy, emotions play an important role both through their associa...

A bibliometric analysis of the current state of research on family interventions for ASD.

UNLABELLED: In recent years, family intervention has become a hot research direction in the field of...

Machine Learning Models to Identify Clinically Significant Anxiety in Short-Term Insomnia Using Accelerometers.

Clinically significant anxiety (CSA) is common in individuals with short-term insomnia. This study a...

Developing a machine learning algorithm to predict psychotropic drugs-induced weight gain and the effectiveness of anti-obesity drugs in patients with severe mental illness: Protocol for a prospective cohort study.

Obesity is a global public health concern, often co-occurring in patients with severe mental illness...