Psychiatry

Reliability Auditing for Downstream LLM tasks in Psychiatry: LLM-Generated Hospitalization Risk Scores

Large language models (LLMs) are increasingly utilized in clinical reasoning and risk assessment. However, their interpretive reliability in critical and indeterminate domains such as psychiatry remains unclear. Prior work has identified algorithmic biases and prompt sensitivity in these systems, raising concerns about how contextual information may influence model outputs, but there remains no sy...

Optimal Question Selection from a Large Question Bank for Clinical Field Recovery in Conversational Psychiatric Intake

Psychiatric intake is a sequential, high-stakes information-gathering process in which clinicians must decide what to ask, in what order, and how to interpret incomplete or ambiguous responses under limited time. Despite growing interest in conversational AI for healthcare, there is still limited infrastructure for conversational AI in this application. Accordingly, we formulate this task as a que...

Patient perspectives on living with hypertension: Social media listening analysis across predominantly high-income countries

Background: Chronic conditions such as hypertension can significantly disrupt daily life and emotional wellbeing. The interaction between patients' pe...

Predicting Depressive Symptoms Among Reproductive-Aged Women in Bangladesh Using Bagging Ensemble Machine Learning on Imbalanced Bangladesh Demographic and Health Survey 2022 Data

Background Depressive symptoms among reproductive-aged women represent a major public health concern in low- and middle-income countries, yet systemat...

Improving clinical interpretability of linear neuroimaging models through feature whitening

Linear models are widely used in computational neuroimaging to identify biomarkers associated with brain pathologies. However, interpreting the learne...

Modeling Higher-Order Brain Interactions via a Multi-View Information Bottleneck Framework for fMRI-based Psychiatric Diagnosis

Resting-state functional magnetic resonance imaging (fMRI) has emerged as a cornerstone for psychiatric diagnosis, yet most approaches rely on pairwis...

Evaluating Large Language Models for Transparent Quality-of-Care Measurement in Children with ADHD

ImportanceGuideline-concordant care for young children with attention-deficit/hyperactivity disorder (ADHD) includes recommending parent training in b...

Sustaining Control and Agency Under Threat: Computational Pathways to Persistence and Escape

Adaptive behavior requires deciding when to persist and when to disengage under uncertainty and partial outcome control. Avoidance has often been stud...

IatroBench: Pre-Registered Evidence of Iatrogenic Harm from AI Safety Measures

Ask a frontier model how to taper six milligrams of alprazolam (psychiatrist retired, ten days of pills left, abrupt cessation causes seizures) and it...

Unlocking Multi-Site Clinical Data: A Federated Approach to Privacy-First Child Autism Behavior Analysis

Automated recognition of autistic behaviors in children is essential for early intervention and objective clinical assessment. However, the developmen...

How and why does deep ensemble coupled with transfer learning increase performance in bipolar disorder and schizophrenia classification?

Transfer learning (TL) and deep ensemble learning (DE) have recently been shown to outperform simple machine learning in classifying psychiatric disor...

Grading of Erythema and Visual Attributes in Atopic Dermatitis across Diverse Skin Tones Using a Vision AI Pipeline

Background: Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disease associated with clinical, psychosocial, and economic burden. Accur...

Developing a Tiered Machine Learning Alert System for Real-Time Suicide Risk Detection in a Digital Mental Health Setting

The goal of this work was to leverage a large corpus of text based psychotherapy data to create novel machine learning algorithms that can identify su...

Elder-Sim: A Psychometrically Validated Platform for Personality-Stable Elderly Digital Twins

Background: LLMs enable patient-facing conversational agents, creating a pathway toward digital twins that capture older adults' lived experiences and...

MAMGL: A memory-augmented meta-graph learning framework for adolescent major depression disorder diagnosis

Adolescent major depressive disorder (AMDD) is a prevalent and heterogeneous psychiatric condition that emerges during a critical period of brain deve...

D-GATNet: Interpretable Temporal Graph Attention Learning for ADHD Identification Using Dynamic Functional Connectivity

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder whose neuroimaging-based diagnosis remains challenging due ...

DuSCN-FusionNet: An Interpretable Dual-Channel Structural Covariance Fusion Framework for ADHD Classification Using Structural MRI

Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition; however, its neurobiological diagnosis remains cha...

Data Diversity vs. Model Complexity in the Prediction of Pediatric Bipolar Disorder: Evidence from Academic and Community Clinical Samples

Pediatric bipolar disorder is challenging to diagnose accurately due to symptom heterogeneity. More standardized and data-driven approaches are needed...

Identification of Suicide-Related Subgroups Using Latent Class Analysis: Complementary Insights to Explainable AI-Based Classification

Purpose: Suicide and self-harm are major public health concerns characterized by substantial clinical and psychosocial heterogeneity. While latent cla...

Narcolepsy Revolution - Protocol and Methodology A diagnostic accuracy study protocol using the Dreem 3 headband for ambulatory diagnosis of narcolepsy in children and young adults

Background Narcolepsy is a rare, lifelong neurological disorder that often begins in childhood or adolescence. Diagnosis is frequently delayed because...