Psychiatry

Prediction and Prevalence of Self-Harm and Nonsuicidal Self-Injury in Children With Learning Disabilities: A Machine-Learning Approach in Saudi Arabia.

The current study aimed to estimate the prevalence of self-harm and nonsuicidal self-injury (NSSI) in children with learning disabilities (LD) in Saudi Arabia and to develop machine-learning (ML) models to identify individuals at elevated risk. In a cross-sectional study, 392 children with DSM-5 specific LD (aged 8-12 years) were recruited through clinical and community channels and assessed for l...

The Role of Artificial Intelligence in Clinical Psychology: How AI and NLP Systems Are Reshaping Psychological Interventions. A Systematic Review.

Artificial Intelligence (AI) technologies are rapidly evolving and their integration into psychological practices has progressively expanded, offering new tools for diagnosis, treatment and therapeutic monitoring. This review examines the transformative role of AI, particularly Natural Language Processing (NLP) systems, in reshaping clinical psychology and digital mental health interventions (DMHI...

Artificial intelligence robots for mental health applications: a scoping review.

BACKGROUND: The contradiction between the surging demand for mental health services and the shortage of professional resources is becoming increasingl...

Using machine learning-based Natural Language Processing to quantify emergency department presentations related to suicide or self-harm in the Australian Capital Territory.

BACKGROUND: Suicide and self-harm are significant issues globally. Accurate, efficient and comprehensive data are required to identify people who pres...

Integration of AI and Neuroscience: New Methods in Studying the Causes of Mental Disorders.

ObjectiveThis review sought to systematize knowledge about the use of artificial intelligence in neurobiological research of mental disorders and asse...

An Interpretable Functional-Dynamic Synaptic Graph Neural Network for Major Depressive Disorder Diagnosis from rs-fMRI.

Major depressive disorder (MDD) is a serious, complex psychiatric condition that affects millions of people worldwide. Early diagnosis and biomarker i...

Decoding Naturalistic Episodic Memory with Artificial Intelligence and Brain-Machine Interface.

Episodic memory integrates what, where, and when of experience into a coherent autobiographical narrative. Decades of research have identified hippoca...

Factors associated with job finding anxiety among senior health sciences students: the role of attitudes toward artificial intelligence.

BACKGROUND: Today, university students are experiencing increasing anxiety about finding a job after graduation. One of the factors intensifying this ...

SAFEGUARD: Transforming Military Suicide Prevention Through Predictive Analytics and Targeted Interventions.

PURPOSE OF REVIEW: Machine learning predictive modeling can support scalable prevention of suicide-related behavior (SRB). SAFEGUARD is a three-pronge...

[AI Technologies for Diagnostics and Conversation Analysis in Psychotherapy: Opportunities and Challenges].

In recent years, artificial intelligence (AI) has made great progress in medicine, particularly in the analysis of medical imaging data. Its potential...

Diagnostic Utility of Formal Thought Disorder Between Affective and Non-Affective Psychosis: A Machine Learning Approach.

Integrating Multimodal Neuroimaging and Physical-Health Markers for Autism Spectrum Disorder in the ABCD Study.

BACKGROUND: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by diverse presentations, which complicates the ide...

Predicting suicide attempts among the elderly: A machine learning evaluation of a staged ideation-to-action approach based on the Three-Step Theory.

Suicide among older adults remains a major global public health concern, particularly acute in South Korea. Effective prevention requires prediction m...

Predicting the Course of High-Impact Chronic Pain Using Machine Learning Algorithms.

High-impact chronic pain (HICP) affects over 17 million U.S. adults and follows highly variable courses. To date, the relative importance of biopsycho...

Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging.

For decades, psychiatric neuroimaging has searched for biomarkers of depression and other disorders, but they remain elusive in clinical practice. Whi...

Harnessing exosomes for precision diagnostics and therapies in psychiatry disorders.

Exosomes are nanosized extracellular vesicles enriched with proteins, lipids, and nucleic acids and are emerging as powerful mediators of intercellula...

Changes in personality functioning following psychotherapy: Utilizing machine learning to identify predictors in a psychodynamic psychotherapy sample.

INTRODUCTION: Although personality functioning has a long psychodynamic tradition and has received renewed interest in psychotherapy research with the...

Ethical challenges of using artificial intelligence in suicide prevention: a literature review.

Artificial intelligence (AI) is a tool that could provide useful prevention strategies for people at risk of suicide. However, there are many ethical ...

Early hemodynamic phenotyping in sepsis using transthoracic echocardiography: A proof-of-concept study in a north African ICU.

BACKGROUND: Cardiovascular dysfunction in sepsis is heterogeneous and contributes to poor outcomes. Hemodynamic phenotyping may delineate pathophysiol...

A dual attention transformer modelling for explainable mental health analysis in academic environments using TaBERT.

Mental health plays a crucial role in shaping students' academic performance and practical outcomes, yet traditional analytical methods often fall sho...