Psychiatry

Machine Learning Prediction of Autism Spectrum Disorder From a Minimal Set of Medical and Background Information.

IMPORTANCE: Early identification of the likelihood of autism spectrum disorder (ASD) using minimal information is crucial for early diagnosis and intervention, which can affect developmental outcomes.

Can digital leadership transform AI anxiety and attitude in nurses?

BACKGROUND: The lack of artificial intelligence applications in nursing education and the nursing profession in Turkey and the need for strategies for integrating artificial intelligence into the nursing profession continues. At this point, there is a need to transform the negative attitudes and anxiety that may occur in nurses.

Improving treatment completion for young adults with substance use disorder: Machine learning-based prediction algorithms.

Substance use disorder (SUD) treatment completion was intertwined with various factors. However, few studies have explored the intersections of psycho...

Predicting the severity of mood and neuropsychiatric symptoms from digital biomarkers using wearable physiological data and deep learning.

Neuropsychiatric symptoms (NPS) and mood disorders are common in individuals with mild cognitive impairment (MCI) and increase the risk of progression...

Neural waves and computation in a neural net model II: Data-like structures and the dynamics of episodic memory.

The computational resources of a neuromorphic network model introduced earlier were investigated in the first paper of this series. It was argued that...

Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records.

Machine Learning models trained from real-world data have demonstrated promise in predicting suicide attempts in adolescents. However, their transport...

Artificial intelligence in the detection and treatment of depressive disorders: a narrative review of literature.

Modern psychiatry aims to adopt precision models and promote personalized treatment within mental health care. However, the complexity of factors unde...

The real ethical issues with AI for clinical psychiatry.

This article explores the ethical issues arising from ordinary AI applications currently used in mental health care, rather than speculative future sc...

Speech and language patterns in autism: Towards natural language processing as a research and clinical tool.

Speech and language differences have long been described as important characteristics of autism spectrum disorder (ASD). Linguistic abnormalities rang...

fNIRS-Driven Depression Recognition Based on Cross-Modal Data Augmentation.

Early diagnosis and intervention of depression promote complete recovery, with its traditional clinical assessments depending on the diagnostic scales...

Natural language processing in at-risk mental states: enhancing the assessment of thought disorders and psychotic traits with semantic dynamics and graph theory.

OBJECTIVE: Verbal communication contains key information for mental health assessment. Researchers have linked psychopathology phenomena to certain co...

Anticipating influential factors on suicide outcomes through machine learning techniques: Insights from a suicide registration program in western Iran.

Suicide is a global public health concern, with increasing rates observed in various regions, including Iran. This study focuses on the province of Ha...

Pivotal response treatment and applied behavior analysis interventions for autism spectrum disorder delivered by human vs robotic agents: a systematic review of literature.

Robotic technology-aided instruction and interventions have been designed to support both Applied Behavior Analysis (ABA) and Pivotal Response Treatme...

Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans.

Measuring suicide risk fluctuation remains difficult, especially for high-suicide risk patients. Our study addressed this issue by leveraging Dynamic ...

Evaluating GenAI systems to combat mental health issues in healthcare workers: An integrative literature review.

BACKGROUND: Mental health issues among healthcare workers remain a serious problem globally. Recent surveys continue to report high levels of depressi...

Semi-autonomous touch method merging robot's autonomous touch and user-operated touch for improving user experience in robot touch.

The demand for therapeutic robots to alleviate mental health problems is growing. Studies have shown that people's mental health improves when they ar...

Diagnostic accuracy of large language models in psychiatry.

INTRODUCTION: Medical decision-making is crucial for effective treatment, especially in psychiatry where diagnosis often relies on subjective patient ...

Advancing ASD identification with neuroimaging: a novel GARL methodology integrating Deep Q-Learning and generative adversarial networks.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects an individual's behavior, speech, and social interaction. Early and accu...

Potential Shared Mitochondrial-Related Gene Signatures and Molecular Mechanisms Between Polycystic Ovary Syndrome (PCOS) and Major Depressive Disorder (MDD): Evidence from Transcriptome Data and Machine Learning.

Polycystic ovary syndrome (PCOS) is strongly associated with major depressive disorder (MDD), but the shared pathophysiological mechanisms between the...

Adaptive node feature extraction in graph-based neural networks for brain diseases diagnosis using self-supervised learning.

Electroencephalography (EEG) has demonstrated significant value in diagnosing brain diseases. In particular, brain networks have gained prominence as ...