Psychiatry

Anxiety about artificial intelligence from patient and doctor-physician.

OBJECTIVE: This paper investigates the anxiety surrounding the integration of artificial intelligence (AI) in doctor-patient interactions, analyzing the perspectives of both patients and healthcare providers to identify key concerns and potential solutions.

Pre-trained artificial intelligence language model represents pragmatic language variability central to autism and genetically related phenotypes.

Many individuals with autism experience challenges using language in social contexts (i.e., pragmatic language). Characterizing and understanding pragmatic variability is important to inform intervention strategies and the etiology of communication challenges in autism; however, current manual coding-based methods are often time and labor intensive, and not readily applied in ample sample sizes. T...

Early Attrition Prediction for Web-Based Interpretation Bias Modification to Reduce Anxious Thinking: A Machine Learning Study.

BACKGROUND: Digital mental health is a promising paradigm for individualized, patient-driven health care. For example, cognitive bias modification pro...

How do machine learning models perform in the detection of depression, anxiety, and stress among undergraduate students? A systematic review.

Undergraduate students are often impacted by depression, anxiety, and stress. In this context, machine learning may support mental health assessment. ...

Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations.

Psychiatric disorders are influenced by genetic and environmental factors. However, their study is hindered by limitations on precisely characterizing...

Multimodal autism detection: Deep hybrid model with improved feature level fusion.

OBJECTIVE: Social communication difficulties are a characteristic of autism spectrum disorder (ASD), a neurodevelopmental condition. The earlier metho...

Comparison between clinician and machine learning prediction in a randomized controlled trial for nonsuicidal self-injury.

BACKGROUND: Nonsuicidal self-injury is a common health problem in adolescents and associated with future suicidal behavior. Predicting who will benefi...

Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study.

OBJECTIVE: Depression has emerged as a global public health concern with high incidence and disability rates, which are timely imperative to identify ...

Evaluating virtual reality technology in psychotherapy: impacts on anxiety, depression, and ADHD.

BACKGROUND: Mental health issues pose a significant challenge for medical providers and the general public. The World Health Organization predicts tha...

Immune-based Machine learning Prediction of Diagnosis and Illness State in schizophrenia and bipolar Disorder: How data bias and overfitting were avoided.

In a letter critiquing our manuscript, Takefuji highlights general pitfalls in machine learning, without directly engaging with our study. The comment...

Real-time assistance in suicide prevention helplines using a deep learning-based recommender system: A randomized controlled trial.

OBJECTIVE: To evaluate the effectiveness and usability of an AI-assisted tool in providing real-time assistance to counselors during suicide preventio...

Artificial Intelligence and Its Revolutionary Role in Physical and Mental Rehabilitation: A Review of Recent Advancements.

The integration of artificial intelligence (AI) technologies into physical and mental rehabilitation has the potential to significantly transform thes...

Identification of autism spectrum disorder using electroencephalography and machine learning: a review.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by communication barriers, societal disengagement, and monotonous actio...

Anxiety in aquatics: Leveraging machine learning models to predict adult zebrafish behavior.

Accurate analysis of anxiety behaviors in animal models is pivotal for advancing neuroscience research and drug discovery. This study compares the pot...

Advancing psychosocial disability and psychosocial rehabilitation research through large language models and computational text mining.

Psychosocial rehabilitation and psychosocial disability research have been a longstanding topic in healthcare, demanding continuous exploration and an...

A novel ECG-based approach for classifying psychiatric disorders: Leveraging wavelet scattering networks.

Individuals with neuropsychiatric disorders experience both physical and mental difficulties, hindering their ability to live healthy lives and partic...

Screening for Depression and Anxiety Using a Nonverbal Working Memory Task in a Sample of Older Brazilians: Observational Study of Preliminary Artificial Intelligence Model Transferability.

BACKGROUND: Anxiety and depression represent prevalent yet frequently undetected mental health concerns within the older population. The challenge of ...

FacialNet: facial emotion recognition for mental health analysis using UNet segmentation with transfer learning model.

Facial emotion recognition (FER) can serve as a valuable tool for assessing emotional states, which are often linked to mental health. However, mental...

Graph Convolutional Network With Self-Supervised Learning for Brain Disease Classification.

Brain functional network (BFN) analysis has become a popular method for identifying neurological diseases at their early stages and revealing sensitiv...

An explainable predictive model for anxiety symptoms risk among Chinese older adults with abdominal obesity using a machine learning and SHapley Additive exPlanations approach.

BACKGROUND: Early detection of anxiety symptoms can support early intervention and may help reduce the burden of disease in later life in the elderly ...