Psychiatry

Decoding how macronutrients influence depressive symptoms: Combined insights from regression, nonlinear modeling, and machine learning: a cross-sectional study using NHANES data.

Dietary management is an essential component of comprehensive depression care;however, studies simultaneously evaluating multiple macronutrients and their associations with depressive symptoms are limited. Data from National Health and Nutrition Examination Survey 2005 to 2023 were analyzed. Multiple linear regression, logistic regression, restricted cubic spline models, and machine learning were ...

Machine learning-based integration of surface morphometric features identifies diagnostic and symptom-related cortical signatures of schizophrenia.

Schizophrenia (SCZ) is associated with widespread cortical abnormalities, however, neuroanatomical markers with robust diagnostic and clinical relevance remain limited. In this study, we integrated multiple surface-based cortical features-cortical thickness, gyrification, sulcal depth, cortical complexity, and surface ratio-from a large multi-site cohort (329 SCZ, 669 healthy controls) and impleme...

Prediction of Clinically Meaningful Improvement After Internet-Delivered Cognitive Behavioral Therapy for Depression and Anxiety Disorders: Machine Learning-Based Predictive Model Development and Temporal Validation Study.

BACKGROUND: Up to 50% of patients treated with internet-delivered cognitive behavioral therapy (ICBT) for depression and anxiety disorders do not expe...

Autism in adolescence and adulthood.

Landmark descriptions of autism in the 1940s were based on clinical work with children. Research over the subsequent 80 years has largely focused on t...

A clinically validated framework for auditing AI chatbot behavior in mental health interactions.

Millions of users turn to consumer artificial intelligence chatbots to discuss emotional, behavioral and mental-health concerns, creating an urgent ne...

Data science and AI in medicine and global health: The need for inter-philosophies dialogue, cross-cultural ethics and ecocentricity.

Advances in data science and medical artificial intelligence (AI) raise complex philosophical and ethical quandaries about what it means to know a per...

Two-Stage Extraction of Clinical Course Information From Psychiatric Discharge Summaries Using Fine-Tuned Large Language Models: Cross-Validation Study.

BACKGROUND: Clinical course information, including disease onset, episode recurrence, and hospitalization history, is essential for psychiatric care a...

Frequency of use and attitudes toward AI-based health counseling among patients attending the dermatology outpatient clinic: a cross-sectional study.

PURPOSE: To quantify the prevalence of pre-visit AI consultation among dermatology outpatients and to assess associated attitudes, anxiety changes, an...

Mortality Research With Artificial Intelligence Tools: Applying Language Models to the National Violent Death Reporting System Suicide Narratives, 2003-2023.

Suicide deaths in the United States have increased for a generation with no current evidence of meaningful decline. With advances in data collection a...

Machine Learning Prediction of First Suicide Attempts in Early Adolescence Among Child Ideators.

OBJECTIVE: Identifying those at highest risk of making a first suicide attempt during adolescence is crucial to inform early suicide prevention. Our s...

Identification of EIF4A1 as a candidate molecular indicator of autism spectrum disorder using integrative bioinformatic and biological methods.

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition with complex genetic and molecular mechanism. Identifying reliable mole...

Data-driven trajectories of atrophy explain clinical heterogeneity across Lewy body diseases.

BACKGROUND: Lewy body diseases (LBD) collectively share α-synuclein Lewy pathology, yet present wide clinical heterogeneity, with overlapping motor an...

Machine Learning Classification of Prevalent Chronic Disease Using Multidimensional Social, Behavioral, and Psychological Determinants: A Cross-Sectional Analysis of the 2024 National Health Interview Survey.

BACKGROUND: Chronic diseases account for approximately 90% of the $4.5 trillion annual healthcare expenditure in the United States. While traditional ...

Deep learning for EEG-based depression detection: A systematic literature review of methods and interpretability techniques.

Depression is a widespread mental illness in which there is a noticeable difference in the quality of life of people. Electroencephalography (EEG) see...

Psychological stress-associated ceramide and diacylglyceride lipotoxicity as contributors to first-episode depression pathophysiology: A neuroimmune-metabolic-oxidative stress (NIMETOX) perspective.

BACKGROUND: Aberrations in neuro-immune, metabolic, and oxidative stress (NIMETOX) pathways are implicated in major depressive disorder (MDD). First-e...

Computational phenotyping and predictive modeling of outcomes using multimodal objective measures in psychiatry.

Clinical decision-making in psychiatry has traditionally relied on rating scales and clinical impressions documented in the electronic health record (...

A Contrastive Framework for Modeling Brain Heterogeneity in Precision Mental Health.

Precision mental health aims to enable personalized care for mental disorders via the identification of brain-behavior associations at the individual ...

Modeling suicidal thoughts and behaviors in university students: integrating suicide theory with interpretable machine learning.

University students are at risk of suicidal thoughts and behaviors (STB), with prevalence rates higher than those in the general population. Machine l...

Bibliometric analysis of ergonomics studies in nursing: Trends and highlights between 1981-2025.

BackgroundErgonomics is important for improving nurses' working conditions and preventing work-related health problems. Research on ergonomics in nurs...

Mental workload classification using EEG in drivers: A personalized approach to occupational risk reduction.

BackgroundMental state is a key factor influencing occupational health and safety, particularly in professional drivers who are exposed to high cognit...