Psychiatry

Anxiety & Stress

Latest AI and machine learning research in anxiety & stress for healthcare professionals.

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Showing 1441-1460 of 4,207 articles

Exploring Stress-Induced Neural Circuit Remodeling through Data-Driven Analysis and Artificial Neural Network Simulation

Chronic stress induces behavioral rigidity and neural circuit remodeling, yet the underlying computational mechanisms remain unclear. In this study, we analyze in vivo GCaMP8s recordings from the amygdala-striatal circuits (Giovanniello et al.) and identify a difference in the recovery dynamics of the BLA-DMS and CeA-DMS pathways following acute perturbations. Through data analysis and artificial ...

Stress Coping Style Alters Functional Brain Network Activity to Acute Stressor

Consistent individual differences in behavior (e.g., personality types, stress coping styles) are a common occurrence across animal taxa. One hypothesis poses that the resulting constraints for within-individual behavioral variation may also lead to constraints for the evolution of behavior. With stress coping styles seen across taxa, it suggests a common underlying proximate mechanism. In this st...

From Patient Voices to Policy: Data Analytics Reveals Patterns in Ontario’s Hospital Feedback

Patient satisfaction is a central measure of high-performing healthcare systems, yet real-world evaluations at scale remain challenging. In this study...

Identifying Psychiatric Manifestations in Outpatients with Depression and Anxiety: A Large Language Model-Based Approach

Accurate psychiatric diagnosis and assessment are crucial for effective treatment. However, while current data-driven approaches emphasize diagnostic ...

The lived experience of social anxiety disorder: A conceptual model based on published literature and social media listening

Social anxiety disorder (SAD) affects up to 1 in 8 individuals over their lifetime and is characterized by an intense fear of social situations where ...

Automated IntraVascular UltraSound Image Processing and Quantification of Coronary Artery Anomalies: The AIVUS-CAA software

Coronary artery anomalies (CAA) with an intramural course are associated with elevated risks of ischemia and sudden cardiac death under stress. Intrav...

The Impact of Negative Emotions on Adolescents’ Nonsuicidal Self-Injury Thoughts: An Integrated Application of Machine Learning and Multilevel Logistic Models

Non-Suicidal Self-Injury (NSSI) is a prevalent and complex behavior among adolescents, often linked to negative emotions such as loneliness, anxiety, ...

Development and validation of a machine learning model to predict cognitive behavioral therapy outcome in obsessive-compulsive disorder using clinical and neuroimaging data

Cognitive behavioral therapy (CBT) is a first-line treatment for obsessive-compulsive disorder (OCD), but clinical response is difficult to predict. I...

Passive sensing with psyche: utilizing data from wearable technology to predict emotion states

Depression and anxiety are some of the most common mental health disorders in the world contributing to significant morbidity and mortality. Past trea...

Dehumanisation and Datafication: Key stressors of Algorithmic Management: A qualitative analysis of Chinese couriers

Technology is an important social determinant of health that has so far been poorly understood. Nevertheless, technologies such as algorithms and arti...

Demonstrating the potential of untargeted hair proteomics for personalized biomarkers in stress-associated disorders

Biomarker research in psychopathology increasingly employs high-dimensional omics approaches. Yet, proteomics based on human hair remain largely unexp...

Blood Immuno-metabolic Biomarker Signatures of Depression and Affective Symptoms in Young Adults

Depression is associated with alterations in immuno-metabolic biomarkers, but it remains unclear whether these alterations are limited to specific mar...

PREACT-digital: Study protocol for a longitudinal, observational multi-center study on wearable- and EMA- based predictors of non-response to CBT for internalizing disorders

Despite CBT’s status as a first-line treatment, a substantial proportion of patients does not experience sufficient symptom relief. Recent advances in...

Identifying Predictors of Benzodiazepine Discontinuation in Medical Cannabis Patients with Post-traumatic Stress Disorder Using a Machine Learning Approach

Post-Traumatic Stress Disorder (PTSD) is a debilitating mental health condition commonly treated with medications like benzodiazepines (BZDs), despite...

LLM-Guided Pain Management: Examining Socio-Demographic Gaps in Cancer vs non-Cancer cases

Large language models (LLMs) offer potential benefits in clinical care. However, concerns remain regarding socio-demographic biases embedded in their ...

A Combined Predictive and Causal Approach for Neighborhood-Level Diabetes Detection

Develop a neighborhood-level framework using machine learning and causal inference to identify socioeconomic and behavioral drivers of Type 2 diabetes...

Artificial Intelligence for Contextual Well-being: Protocol for an Exploratory Sequential Mixed Methods Study with Medical Students as a Social Microcosm

AI-powered conversational agents have proven effective in alleviating psychological distress, however, concerns about autonomy and authentic psycholog...

Unmet Needs in Acute Hepatic Porphyria Diagnosis: A Comparative Big Data Analysis of an AI-based Human-in-the-Loop Screening Versus Standard of Care

Acute Hepatic Porphyria (AHP) is a rare genetic disease characterized by unpredictable life-threatening attacks. There is no reliable biochemical scre...

Towards Understanding Bipolar Disorder Through Social Media and Transformer Models: Challenges and Insights

Social media presents a promising avenue for monitoring mental health, yet detecting bipolar disorder (BD) remains significantly underexplored. The co...

Evaluating Enhanced LLMs for Precise Mental Health Diagnosis from Clinical Notes

Anxiety, depression, and other mental health conditions are affecting millions of people worldwide each year. However, limited access to mental health...

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