Psychiatry

Application of machine learning algorithms to identify risk factors for depression in type 2 diabetes mellitus patients: A Taiwan diabetes registry study.

BACKGROUND: We analyzed variables reported during routine clinical practice using a registrational database to estimate risk factors for depression in people with type 2 diabetes mellitus.

Clinical, genetic, and sociodemographic predictors of symptom severity after internet-delivered cognitive behavioural therapy for depression and anxiety.

BACKGROUND: Internet-delivered cognitive behavioural therapy (ICBT) is an effective and accessible treatment for mild to moderate depression and anxiety disorders. However, up to 50% of patients do not achieve sufficient symptom relief. Identifying patient characteristics predictive of higher post-treatment symptom severity is crucial for devising personalized interventions to avoid treatment fail...

A qualitative analysis of college students' interest in mHealth solutions.

This study explores college students' perceptions of an AI-driven mHealth application designed to promote well-being. With rising mental health challe...

Physical and mental health management for the older adult using XGBoost algorithm supported by new media technology: developing personalized health intervention plans using healthcare data from the CLHLS database.

INTRODUCTION: With the increasing aging population, there is a growing need for precise and intelligent health management solutions tailored to older ...

Early diagnosis of autism across developmental stages through scalable and interpretable ensemble model.

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition that challenges early diagnosis due to its diverse manifestations across...

Evaluating and modifying the PHDI for depression prevention: insights from NHANES 2005-2018.

BACKGROUND: Depression is a significant focus in mental health research, emerging as a pressing public health concern globally. The Planetary Health D...

ChatGPT-4o vs Psychiatrists in Responding to Common Antidepressant Concerns.

PurposeArtificial intelligence (AI) is increasingly integrated into healthcare, including psychiatric care. This study evaluates ChatGPT-4o's reliabil...

Is This Chatbot Safe and Evidence-Based? A Call for the Critical Evaluation of Generative AI Mental Health Chatbots.

The proliferation of artificial intelligence (AI)-based mental health chatbots, such as those on platforms like OpenAI's GPT Store and Character. AI, ...

Beyond the Trend Arrow: Potential Value of Artificial Intelligence-Supported Glucose Predictions for People with Type 1 Diabetes Using Continuous Glucose Monitoring Systems.

Advances in diabetes technologies such as continuous glucose monitoring (CGM) have provided significant opportunities to improve glycemic and quality-...

Inflammatory biomarkers as predictors for unlocking antidepressant efficacy: Assessing predictive value and risk stratification in major depressive disorder in a prospective longitudinal study.

BACKGROUND: Major depressive disorder (MDD) is characterized by significant heterogeneity in treatment response, with inflammation hypothesized to pla...

Contrastive functional connectivity defines neurophysiology-informed symptom dimensions in major depression.

Major depressive disorder (MDD) is highly heterogeneous, posing challenges for effective treatment due to complex interactions between clinical sympto...

Brain stimulation outcome prediction in Major Depressive Disorder by deep learning models using EEG representations.

Major Depressive Disorder (MDD) is known as a widespread illness and needs a timely treatment. The treatment procedure is currently based on the trial...

Machine Learning and Artificial Intelligence in Suicide Prevention: A Bibliometric Analysis of Emerging Trends and Implications for Nursing.

Nurses play a crucial role in suicide prevention, yet the integration of artificial intelligence and machine learning technologies into nursing practi...

Predicting rTMS treatment response in depression: use of machine learning models to identify the roles of metabolic and clinical factors.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression in patients with major depressive disorder (M...

Detecting Tardive Dyskinesia Using Video-Based Artificial Intelligence.

Tardive dyskinesia (TD) is a late-onset adverse effect of dopamine receptor-blocking medications, characterized by involuntary movements primarily af...

Occupational mental health: an investigation of risk indicators using interpretable machine learning techniques.

OBJECTIVE: To apply interpretable machine learning to identify key factors influencing work-related mental health cases to support early intervention.

Functional connectome-based predictive modeling of suicidal ideation.

Suicide represents an egregious threat to society despite major advancements in medicine, in part due to limited knowledge of the biological mechanism...

A scientometric analysis of machine learning in schizophrenia neuroimaging: Trends and insights (2012-2024).

Machine learning applications in schizophrenia neuroimaging research have undergone significant evolution since 2012. However, a comprehensive sciento...

Machine learning models of depression in middle-aged and older adults with cardiovascular metabolic diseases.

BACKGROUND: The incidence of cardiovascular metabolic diseases (CMD) is increasing, and depression in CMD patients significantly impacts prognosis. Th...

Hemoglobin - to - red cell distribution width ratio in depression symptoms: Threshold effects and metabolic - inflammatory mediation revealed by multimodal machine learning and symptom network analysis in 196,260 adults.

OBJECTIVE: To investigate the association between hemoglobin to red blood cell distribution width ratio (HRR) and depression symptoms.