Psychiatry

Development of a natural language processing application to extract and categorize mentions of violence from mental healthcare records text

Background: Experiences of violence are reported frequently by mental health service users, victims of violence are at a greater risk of mental health disorders, and violence may sometimes occur as a consequence of a mental disorder. Electronic health records (EHRs) are an important source of information about healthcare, and its social context. Occurrences of violence are not routinely recorded a...

Knowledge-Guided Retrieval-Augmented Generation for Zero-Shot Psychiatric Data: Privacy Preserving Synthetic Data Generation

AI systems in healthcare research have shown potential to increase patient throughput and assist clinicians, yet progress is constrained by limited access to real patient data. To address this issue, we present a zero-shot, knowledge-guided framework for psychiatric tabular data in which large language models (LLMs) are steered via Retrieval-Augmented Generation using the Diagnostic and Statistica...

Social Determinants of Health and Chronic Disease Risk Prediction in the All of Us Research Program

Social determinants of health (SDoH), the social, economic, and environmental conditions shaping health trajectories, contribute to chronic disease ri...

Aggregate benchmark scores obscure patient safety implications of errors across frontier language models

Frontier language models are widely used for health-related queries, yet aggregate benchmark scores do not capture safety implications of errors. We a...

Causal differential expression analysis under unmeasured confounders with causarray

Advances in single-cell sequencing and CRISPR technologies have enabled detailed case-control comparisons and experimental perturbations at single-cel...

Beyond AI Psychosis and Sycophancy: Structural Drift as a System-Level Safety Failure

Background: Conversational AI safety systems are primarily evaluated using message-level content monitoring, which assesses inputs and outputs in isol...

Parallel circuits in the posterior parietal cortex balance behavioral flexibility and stability

Rapid behavioral adaptation requires the brain to solve a fundamental computational dilemma: how to flexibly update learned rules while maintaining st...

Neural signatures of impaired semantic contextualization in Autism Spectrum Disorder

Some accounts of the etiology of autism emphasize core impairments in predictive coding, or, more fundamentally, integration of contextual information...

A Scoping Review of AI-Driven Digital Interventions in Mental Health Care: Mapping Applications Across Screening, Support, Monitoring, Prevention, and Clinical Education

Artificial intelligence (AI)-enabled digital interventions, including Generative AI (GenAI) and Human-Centered AI (HCAI), are increasingly used to exp...

Bayesian Inference of Psychometric Variables From Brain and Behavior in Implicit Association Tests

Objective. We establish a principled method for inferring mental health related psychometric variables from neural and behavioral data using the Impli...

A widespread electrical brain network encodes anxiety in health and depressive states

In rodents, anxiety is characterized by heightened vigilance during low-threat and uncertain situations. Though activity in the frontal cortex and lim...

A twin-aware multimodal deep learning framework with optimized late fusion for early prediction of adolescent anxiety disorder

Mental health related problems in adolescents are not always properly evaluated because of incomplete evaluation methods that do not combine biologica...

Multimodal Connectome Fusion via Cross-Attention for Autism Spectrum Disorder Classification Using Graph Learning

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by atypical functional brain connectivity and subtle structural...

Predicting cognitive-behavioral therapy outcomes in obsessive-compulsive disorder from inhibitory control neural activity: A mega-analysis and machine learning study from the ENIGMA-OCD consortium

Objective Cognitive behavioral therapy (CBT) is an effective first-line treatment for obsessive-compulsive disorder (OCD), yet it remains difficult to...

Multi-Criteria Validation of LLM-Inferred Depression Severity from Outpatient Psychiatry Notes

Background: Longitudinal measurement of depression severity in outpatient psychiatric care is limited by infrequent standardized assessments. Although...

IDRL: An Individual-Aware Multimodal Depression-Related Representation Learning Framework for Depression Diagnosis

Depression is a severe mental disorder, and reliable identification plays a critical role in early intervention and treatment. Multimodal depression d...

Distinct cellular DNA methylation mechanisms underlie common and rare genetic risk for brain disorders

Noncoding genetic variation contributes to brain disorder risk, but the mechanisms through which it acts in specific brain cell types remain unclear. ...

Joint Imaging-ROI Representation Learning via Cross-View Contrastive Alignment for Brain Disorder Classification

Brain imaging classification is commonly approached from two perspectives: modeling the full image volume to capture global anatomical context, or con...

Extracting patient reported cannabis use and reasons for use from electronic health records: a benchmarking study of large language models

Objective To develop and evaluate a scalable and reproducible natural language processing (NLP) approach using large language models (LLM), to identif...

A normative reference for large-scale human brain dynamics across the lifespan

Human brain function emerges from dynamic reconfigurations of large-scale neural networks. While population-level reference charts have transformed th...