Anxiety & Stress

One does not fit all: Detecting work-related stress from mouse, keyboard, and cardiac data in the field

Continuously and unobtrusively monitoring work-related stress may help combat its detrimental effects on mental and physical health. For work in offices specifically, mouse and keyboard data have been suggested as highly suitable data sources for stress detection, in addition to physiological data such as heart rate variability (HRV). However, previous studies have yielded mixed results regarding ...

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 develop targeted diagnostic and treatment tools. Given the substantial evidence of variation in mental health symptomatology, efforts to develop prevention and intervention strategies may benefit from machine learning based investigations of individua...

Key features associated with opioid misuse in chronic pain: A machine learning cross-sectional study

Opioid misuse remains a critical public health concern, associated with increased risk of overdose, psychiatric comorbidity, and societal costs. While...

Altered microbial carbohydrate metabolism is associated with anxiety and gastrointestinal symptoms in patients with Generalized Anxiety Disorder

Generalized anxiety disorder (GAD) is a common psychiatric condition, with unknown etiology and pathophysiology. Recent studies have suggested alterat...

Predicting Intentional Self-Harm Following Psychiatric Discharge in Catalonia, Spain: Machine Learning Models from Linked Registry Data

Patients recently discharged from psychiatric hospitalization are at increased risk of intentional self-harm, including suicide. Using linked populati...

Digital phenotyping using wearable-determined physical behaviors and machine learning to detect depression and anxiety in a general population

Depression and anxiety are widespread mental health disorders, yet their diagnosis remains challenging. Digital phenotyping with wearable devices prov...

Development, System Design, Safety, and Performance Metrics of a Conversational Agent for Reducing Depressive and Anxious Symptoms Based on a Large Language Model: The MHAI Study

Conversational agents based on large language models (LLMs) have shown moderate efficacy in reducing depressive and anxiety symptoms. However, most ex...

A randomized clinical trial reveals effects of mindfulness and slow breathing on plasma amyloid beta levels

Prior research suggests that meditation may slow brain aging and reduce the risk of Alzheimer’s disease (AD). However, we lack research systematically...

Clinical trials in depression: Integrated collection across EU and US registries

Depression affects millions worldwide with both pharmacological and psychological therapies widely applied, both with limited treatment success. Many ...

Mining medical narratives on geriatric falls to predict post-fall hospitalization via survival models and large language models

Timely admission to the emergency department is a crucial determinant of patient outcomes. Conversely, unnecessary hospital admissions can overburden ...

Autonomous conversational agents for loneliness, social isolation, depression and anxiety in older people without cognitive impairment: Systematic review and meta-analysis

Loneliness is a major psychological challenge in older adulthood, contributing to increased risks of depression, anxiety, and mortality. Conversationa...

Prediction of Adolescent Internalizing Disorder Risk: Evidence from the Norwegian Mother, Father, and Child Cohort Study

Internalizing disorders are among the most common psychiatric conditions in adolescence, often associated with long-term adverse outcomes. Early ident...

The Silent Signal: Unmasking Myocardial Ischemia in a Resting Heartbeat with Machine Learning

Ischemic heart disease (IHD) remains the leading cause of morbidity and mortality worldwide, imposing a staggering burden on healthcare systems and so...

An informatics approach to profiling patient experiences using electronic health records: constructing and clustering the burden space of individuals under 65 years of age with multiple long-term conditions

Living with multiple long-term conditions (MLTC) profoundly impacts patients’ lives, affecting not only their health but also their financial, emotion...

Effect and Mechanisms of a Voice-based Coach using AI on Psychological Distress: A Phase 2 Randomized Trial

Artificial Intelligence (AI) voice applications have the potential to address the unmet treatment needs among patients with depression and anxiety, bu...

Detecting Mental Disorders in Social Media Using a Transformer-Based Ensemble of Binary Classifiers

This study introduces a novel transformer-based ensemble framework for the multi-label detection of mental health disorders from social media posts. U...

Antidepressant Use at the Threshold: using electronic health records to characterise people prescribed antidepressants around the time of dementia diagnosis

Antidepressant use is common in people with dementia. Antidepressants may be started to manage symptoms of dementia, rather than depressive and anxiet...

Intimate partner violence and stress-related disorders: from epigenomics to resilience.

Intimate Partner Violence (IPV) is a major public health problem to be addressed with innovative and interconnecting strategies for ensuring the psych...

Predicting Suicidal Ideation Among Youths With Autism Spectrum Disorder: An Advanced Machine Learning Study.

This study aimed to predict suicidal ideation among youth with autism spectrum disorder (ASD) by applying machine learning techniques. A cross-section...

Machine Learning Models to Identify Clinically Significant Anxiety in Short-Term Insomnia Using Accelerometers.

Clinically significant anxiety (CSA) is common in individuals with short-term insomnia. This study aims to explore the relationship between CSA and th...