Psychiatry

Large language models in breast cancer reconstruction: A framework for patient-specific recovery and predictive insights.

Breast cancer reconstruction, a vital part of comprehensive cancer therapy, can be performed concurrently with cancer resection, improving both physical and psychological recovery for patients. However, the intricacy and variety of recovery demand a specialized strategy. Thus, a unique framework that uses Natural Language Processing (NLP) and Large Language Models (LLMs) is developed to improve pa...

Accelerating autism spectrum disorder care: A rapid review of data science applications in diagnosis and intervention.

Integrating data science techniques, including machine learning, natural language processing, and big data analytics, has revolutionized the diagnosis and intervention landscape for Autism Spectrum Disorder (ASD). This rapid review examines these approaches' current applications, benefits, limitations, and ethical considerations while identifying key research gaps and future directions. Data-drive...

Integrating generative AI with neurophysiological methods in psychiatric practice.

This paper explores the potential integration of generative AI (e.g., large language models) with neuroscientific and physiological approaches in psyc...

Advances in EEG-based detection of Major Depressive Disorder using shallow and deep learning techniques: A systematic review.

The contemporary diagnosis of Major Depressive Disorder (MDD) primarily relies on subjective assessments and self-reported measures, often resulting i...

Question-based computational language approach outperform ratings scale in discriminating between anxiety and depression.

Major Depression (MD) and General Anxiety Disorder (GAD) are the most common mental health disorders, which typically are assessed quantitatively by r...

Concise multi-class anxiety disorder risk assessment: A novel advanced machine learning approach.

Rapidly assessing anxiety disorder risk is crucial for effective mental health screen and intervention. However, traditional survey tools such as DASS...

The need for research on AI-driven social media and adolescent mental health.

The increasing integration of artificial intelligence (AI) in social media platforms has transformed digital interactions, particularly among adolesce...

A novel framework for seasonal affective disorder detection: Comprehensive machine learning analysis using multimodal social media data and SMOTE.

Seasonal Affective Disorder (SAD) is a mood disorder characterized by recurring depressive episodes during specific seasons, particularly in Fall and ...

Postoperative self-care ability of continuous nursing based on artificial intelligence for stroke patients with neurological injury.

According to the statistics of relevant data, stroke is a relatively common cerebrovascular disease, and its incidence rate is as high as 185/100,000 ...

Suicide ideation detection based on documents dimensionality expansion.

Accurate and secure classifying informal documents related to mental disorders is challenging due to factors such as informal language, noisy data, cu...

Robot or human musicians? The modulating role of perceived performer on how music influences food choices.

Previous research has shown that music robots may reshape people's perceptions of music and health-related behaviors. We investigated how the perceive...

Addressing Workforce and Ethical Gaps in AI-Driven Mental Health Care: A Response to Higgins and Wilson.

Artificial intelligence (AI)-based clinical decision support systems (CDSS) hold great promise for mental health (MH) care, offering opportunities to ...

Neuroimaging and machine learning in eating disorders: a systematic review.

PURPOSE: Eating disorders (EDs), including anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED), are complex psychiatric condi...

Selection of AI model for predicting disability diseases through bipolar complex fuzzy linguistic multi-attribute decision-making technique based on operators.

The selection of suitable AI models to predict disability diseases stands as a vital multi-attribute decision-making (MADM) task within healthcare tec...

Data independent acquisition proteomics and machine learning reveals that proteins associated with immunity are potential molecular markers for early diagnosis of autism.

BACKGROUND: Early diagnosis of autism is critical to its treatment, but so far, there is no clear molecular marker for early diagnosis in children.

Smartphone digital phenotyping in mental health disorders: A review of raw sensors utilized, machine learning processing pipelines, and derived behavioral features.

With increased access to digital technology, there has been a surge in the use of and interest in digital phenotyping as a tool to calculate various f...

Mental disorder preventing by worry levels detection in social media using deep learning based on psycho-linguistic features: application on the COVID-19 lockdown period.

BACKGROUND: The COVID-19 pandemic has had a profound effect on the daily routines of individuals and has influenced various facets of society, includi...

A novel deep neural network approach to detect and monitor cocaine drug abuse.

PURPOSE: Cocaine is one of the most commonly used drugs that may lead to physical and mental health problems. It is necessary to identify individuals ...

A novel artificial intelligence-based methodology to predict non-specific response to treatment.

Non-specific response to treatment (NSRT) is the primary contributor to the failure of randomized clinical trials in major depressive disorder (MDD). ...

Predictors of smartphone addiction in adolescents with depression: combing the machine learning and moderated mediation model approach.

Smartphone addiction (SA) significantly impacts the physical and mental health of adolescents, and can further exacerbate existing mental health issue...