Psychiatry

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 ...

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...

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 interventi...

Exploring pesticide risk in autism via integrative machine learning and network toxicology.

Autism Spectrum Disorder (ASD) is a prevalent neurodevelopmental condition influenced by both geneti...

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...

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 assessm...

Assessing bias in AI-driven psychiatric recommendations: A comparative cross-sectional study of chatbot-classified and CANMAT 2023 guideline for adjunctive therapy in difficult-to-treat depression.

The integration of chatbots into psychiatry introduces a novel approach to support clinical decision...

Combining Machine Learning and Comparative Effectiveness Methodology to Study Primary Care Pharmacotherapy Pathways for Veterans With Depression.

OBJECTIVES: To demonstrate an innovative method combining machine learning with comparative effectiv...

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 c...

Integrating generative AI with neurophysiological methods in psychiatric practice.

This paper explores the potential integration of generative AI (e.g., large language models) with ne...

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 cl...

Comment on "Ai-Aun Chatbot: A Pilot Study on the Effectiveness of an Artificial Intelligence Intervention for Mental Health Among Thai Older Adults".

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 bi...

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 concurr...

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 ...

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...

Clinician Suicide Risk Assessment for Prediction of Suicide Attempt in a Large Health Care System.

IMPORTANCE: Clinical practice guidelines recommend suicide risk screening and assessment across beha...

Ai-Aun Chatbot: A Pilot Study on the Effectiveness of an Artificial Intelligence Intervention for Mental Health Among Thai Older Adults.

Mental health disorders are a significant concern for older adults. Technology has the potential to ...

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 di...

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 mo...

Estimating Treatment Effect Heterogeneity in Psychiatry: A Review and Tutorial With Causal Forests.

BACKGROUND: Flexible machine learning tools are increasingly used to estimate heterogeneous treatmen...