Anxiety & Stress

Counterfactual Stress Testing for Image Classification Models

Deep learning models in medical imaging often fail when deployed in new clinical environments due to distribution shifts in demographics, scanner hardware, or acquisition protocols. A central challenge is underspecification, where models with similar validation performance exhibit divergent real-world failure modes. Although stress testing has emerged as a tool to assess this, current methods typi...

Forecasting Minute-by-Minute Stress, Anxiety, and Affective States Using Time-Series Analysis of Wearable Sensor Data

This paper focuses on forecasting minute-by-minute stress, anxiety, and affective states using wearable sensor data. It addresses mental health as a growing concern and the limitations of traditional assessment methods. A time-series machine learning framework was developed using electrodermal activity (EDA) and heart rate variability (HRV) features from the WESAD dataset. Models were trained and ...

Artificial Intelligence Agents in Mental Health: A Systematic Review and Meta Analysis

The rapid rise of large language models (LLMs) and foundation models has accelerated efforts to build artificial intelligence (AI) agents for mental h...

One Perturbation, Two Failure Modes: Probing VLM Safety via Embedding-Guided Typographic Perturbations

Typographic prompt injection exploits vision language models' (VLMs) ability to read text rendered in images, posing a growing threat as VLMs power au...

Assessing physiological coherence in stress related predictions of large language models: a surrogate based analysis of the Mistral 3 family using wearable HRV data

Chronic psychological stress contributes to allostatic load and is associated with cardiovascular, metabolic, and mental health disorders. Wearable de...

BETA: Resting-state fMRI Biotypes for tDCS Efficacy in Anxiety Among Older Adults At Risk For Alzheimer's Disease

Anxiety is usually gauged by self-report, yet a single symptom level can reflect disparate neural circuitry. In Alzheimer's disease and related dement...

Mucus transcriptional profiling as a minimally invasive approach to identify thermal stress in a stenothermal salmonid

Global climate change has increased the frequency and severity of stressful temperatures that freshwater fishes experience, necessitating rapid and se...

Uncovering Latent Patterns in Social Media Usage and Mental Health: A Clustering-Based Approach Using Unsupervised Machine Learning

The widespread adoption of social media has heightened interest in its psychological effects, particularly on mental health indicators such as anxiety...

Breakout-picker: Reducing false positives in deep learning-based borehole breakout characterization from acoustic image logs

Borehole breakouts are stress-induced spalling on the borehole wall, which are identifiable in acoustic image logs as paired zones with near-symmetry ...

MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models

In the progress of industrial anomaly detection, general anomaly detection (GAD) is an emerging trend and also the ultimate goal. Unlike the conventio...

4D Vessel Reconstruction for Benchtop Thrombectomy Analysis

Introduction: Mechanical thrombectomy can cause vessel deformation and procedure-related injury. Benchtop models are widely used for device testing, b...

Stress Estimation in Elderly Oncology Patients Using Visual Wearable Representations and Multi-Instance Learning

Psychological stress is clinically relevant in cardio-oncology, yet it is typically assessed only through patient-reported outcome measures (PROMs) an...

Extending ZACH-ViT to Robust Medical Imaging: Corruption and Adversarial Stress Testing in Low-Data Regimes

The recently introduced ZACH-ViT (Zero-token Adaptive Compact Hierarchical Vision Transformer) formalized a compact permutation-invariant Vision Trans...

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...

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 ac...

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...

Interpretable machine learning meets systems biology to decode genotype-phenotype maps

Resolving causal genes from quantitative trait loci (QTL) remains fundamentally limited by linkage disequilibrium. We developed an interpretable machi...

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...

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...