Anxiety & Stress

Understanding importance of clinical biomarkers for diagnosis of anxiety disorders using machine learning models.

Anxiety disorders are a group of mental illnesses that cause constant and overwhelming feelings of anxiety and fear. Excessive anxiety can make an individual avoid work, school, family get-togethers, and other social situations that in turn might amplify these symptoms. According to the World Health Organization (WHO), one in thirteen persons globally suffers from anxiety. It is high time to under...

A comprehensive review and analysis of supervised-learning and soft computing techniques for stress diagnosis in humans.

Stress is the most prevailing and global psychological condition that inevitably disrupts the mood and behavior of individuals. Chronic stress may gravely affect the physical, mental, and social behavior of victims and consequently induce myriad critical human disorders. Herein, a review has been presented where supervised learning (SL) and soft computing (SC) techniques used in stress diagnosis h...

An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals.

The economic and personal consequences that a car accident generates for society have been increasing in recent years. One of the causes that can gene...

Tracking stress via the computer mouse? Promises and challenges of a potential behavioral stress marker.

Computer mouse tracking offers a simple and cost-efficient way to gather continuous behavioral data and has mostly been utilized in psychological scie...

Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals.

Mental stress can lead to traffic accidents by reducing a driver's concentration or increasing fatigue while driving. In recent years, demand for meth...

A first perturbome of Pseudomonas aeruginosa: Identification of core genes related to multiple perturbations by a machine learning approach.

Tolerance to stress conditions is vital for organismal survival, including bacteria under specific environmental conditions, antibiotics, and other pe...

Reducing negative emotions in children using social robots: systematic review.

BACKGROUND: For many children, visiting the hospital can lead to a state of increased anxiety. Social robots are being explored as a possible tool to ...

Predicting the 9-year course of mood and anxiety disorders with automated machine learning: A comparison between auto-sklearn, naïve Bayes classifier, and traditional logistic regression.

BACKGROUND: Predicting the onset and course of mood and anxiety disorders is of clinical importance but remains difficult. We compared the predictive ...

A deep learning-integrated micro-CT image analysis pipeline for quantifying rice lodging resistance-related traits.

Lodging is a common problem in rice, reducing its yield and mechanical harvesting efficiency. Rice architecture is a key aspect of its domestication a...

Modeling anxiety and fear of COVID-19 using machine learning in a sample of Chinese adults: associations with psychopathology, sociodemographic, and exposure variables.

OBJECTIVES: Research during prior virus outbreaks has examined vulnerability factors associated with increased anxiety and fear.

Mediating artificial intelligence developments through negative and positive incentives.

The field of Artificial Intelligence (AI) is going through a period of great expectations, introducing a certain level of anxiety in research, busines...

Conversational Agents for Chronic Disease Self-Management: A Systematic Review.

We conducted a systematic literature review to assess how conversational agents have been used to facilitate chronic disease self-management. The Pref...

Towards a new model and classification of mood disorders based on risk resilience, neuro-affective toxicity, staging, and phenome features using the nomothetic network psychiatry approach.

Current diagnoses of mood disorders are not cross validated. The aim of the current paper is to explain how machine learning techniques can be used to...

Stress detection using deep neural networks.

BACKGROUND: Over 70% of Americans regularly experience stress. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and...

Deep learning paired with wearable passive sensing data predicts deterioration in anxiety disorder symptoms across 17-18 years.

BACKGROUND: Recent studies have demonstrated that passive smartphone and wearable sensor data collected throughout daily life can predict anxiety symp...

Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture.

Mindfulness training is associated with improvements in psychological wellbeing and cognition, yet the specific underlying neurophysiological mechanis...

Machine Learning Revealed New Correlates of Chronic Pelvic Pain in Women.

Chronic pelvic pain affects one in seven women worldwide, and there is an urgent need to reduce its associated significant costs and to improve women'...

Using Artificial Intelligence to Predict Change in Depression and Anxiety Symptoms in a Digital Intervention: Evidence from a Transdiagnostic Randomized Controlled Trial.

While digital psychiatric interventions reduce treatment barriers, not all persons benefit from this type of treatment. Research is needed to preempti...

Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning.

Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infr...

Monosodium glutamate induces memory and hepatic dysfunctions in mice: ameliorative role of Jobelyn through the augmentation of cellular antioxidant defense machineries.

This study investigated the effect of high doses of monosodium glutamate (MSG), a known food additive on hepatic, memory and locomotor functions in mi...