Psychiatry

Leveraging Large Language Models and Patient Portal Messages for Early Identification of Depression

Large language model (LLM)-assisted early warning system may help overcome existing barriers to timely depression diagnosis in patients with cardiovascular disease (CVD). This novel application of LLMs to screen patient messages could be applied to other chronic diseases, facilitating automated symptom-driven diagnoses and interventions. To prospectively simulate the impact (change in time to diag...

Unveiling genetic architecture of white matter microstructure through unsupervised deep representation learning of fractional anisotropy maps

Fractional anisotropy (FA) derived from diffusion MRI is a widely used marker of white matter (WM) integrity. However, conventional FA-based genetic studies focus on phenotypes representing tract- or atlas-defined averages, which may oversimplify spatial patterns of WM integrity and thus limit the genetic discovery. Here, we proposed a deep learning–based framework, termed unsupervised deep repres...

Artificial Intelligence-based Automated Echocardiographic Analysis and the Workflow of Sonographers: A randomized crossover trial

This randomized crossover trial aimed to evaluate whether an artificial intelligence (AI)-based automatic analysis tool for echocardiography could imp...

Sleep as a Modifiable Risk Factor for Childhood Autism: Stratified Analysis of U.S. National Survey of Children’s Health Data

This study aimed to examine the association between age-specific sleep sufficiency and autism spectrum disorders (ASD) among U.S. children aged 6–17 y...

AI-Powered Triage of Suicidal Ideation in Adolescents: A Comparative Evaluation of Large Language Models Using Synthetic Clinical Vignettes

To evaluate the performance of leading Large Language Models (LLMs) in classifying suicide risk and generating clinically appropriate action plans for...

Resting-State Functional Connectivity of the Fronto-Limbic and Default Mode Networks as Predictors of Antidepressant Response in Major Depressive Disorder

Major depressive disorder (MDD) is a leading cause of disability worldwide, yet treatment response to antidepressants remains highly variable, with a ...

Reading Between the Signs: Predicting Future Suicidal Ideation from Adolescent Social Media Texts

Suicide is a leading cause of death among adolescents (aged 12–18), yet predicting it remains a significant challenge. Many cases go undetected becaus...

Developing an AI-Enhanced Individualized Prediction Tool for Psychopathological Symptoms in Vietnam: A Study Protocol

Artificial intelligence (AI) is increasingly leveraged in mental healthcare for early detection, monitoring, and personalized intervention. However, m...

Neuroimaging Correlates of Post-Stroke Pain After Ischemic Stroke: Secondary Analysis of the INSPiRE-TMS Trial

Post-stroke pain (PSP) affects nearly half of stroke survivors, severely compromising quality of life. The causes of PSP remain underexplored, althoug...

Machine learning based prediction of high school student mental health

Recent increases in the prevalence rates of anxiety, depression, and suicidal ideation, especially in student populations, present an urgent need to d...

Identifying Key Predictive Features for Opioid Use Disorder Using Machine Learning

Opioid Use Disorder (OUD) continues to pose a pressing public health challenge across the United States, highlighting the critical need for early and ...

Multimodal Speech and Text Models to Detect Suicidal Risks in Adolescents

Early detection of suicide risk in adolescents is crucial but faces challenges including stigma, reluctance to disclose suicidal thoughts, and limited...

Predicting Mental and Psychomotor Delay in Very Pre-term Infants using Large Language Models

Very preterm infants face a considerably higher risk of neurodevelopmental delays, making early diagnosis and timely intervention crucial for improvin...

Predicting Olanzapine Induced BMI increase using Machine Learning on population-based Electronic Health Records

Weight gain is a common side effect in patients treated with olanzapine (N05AH03), contributing to increased risks of metabolic complications such as ...

Large Language Models for Psychiatric Phenotype Extraction from Electronic Health Records

The accurate detection of clinical phenotypes from electronic health records (EHRs) is pivotal for advancing large-scale genetic and longitudinal stud...

Sex-Specific Diagnostic Subtypes in Adolescents Hospitalized for Substance Use Disorders Revealed by Transformer-Based Clustering

Substance use disorders (SUD) are a leading cause of psychiatric hospitalization among adolescents, yet the underlying diagnostic profiles and comorbi...

Classification of familial and non-familial ADHD using auto-encoding network and binary hypothesis testing

Family history is one the most powerful risk factor for attention-deficit/hyperactivity disorder (ADHD), yet no study has tested whether multimodal Ma...

Development and validation of electronic health record-based ascertainment of obsessive-compulsive disorder cases and controls

Obsessive-compulsive disorder (OCD) is a common psychiatric disorder, with two-thirds of affected individuals reporting severe impairment. Despite its...

Responsible AI in Action: Planning through Implementation of a Mortality Model for Palliative Care

Interest in the use of prediction models to support referrals to palliative care is surging. Few high-performing models have been developed, implement...

Signal Mining and Analysis of Adverse Events of Isotretinoin: 20-year real-world pharmacovigilance analysis based on the FAERS database

To identify post-marketing adverse event (AE) signals associated with isotretinoin using real-world data from the U.S. Food and Drug Administration (F...