Anxiety & Stress

From the -Factor to Cognitive Content: Detection and Discrimination of Psychopathologies Based on Explainable Artificial Intelligence.

Differentiating psychopathologies is challenging due to shared underlying mechanisms, such as the -factor. Nevertheless, recent methodological advances suggest that distinct linguistic markers can help detect and differentiate these conditions. This study aimed to use cognitive content analysis with advanced natural language processing (NLP) and machine learning (ML) to (Study 1) distinguish amon...

Artificial intelligence for contextual well-being: Protocol for an exploratory sequential mixed methods study with medical students as a social microcosm.

INTRODUCTION: AI-powered conversational agents have proven effective in alleviating psychological distress, however, concerns about autonomy and authentic psychological development remain, especially in youth during critical stages of identity and resilience formation. Despite the increasing use of AI technologies, there is a significant gap in well-being literacy within educational systems. This ...

Predicting depression severity using machine learning models: Insights from mitochondrial peptides and clinical factors.

Depression presents a significant challenge to global mental health, often intertwined with factors including oxidative stress. Although the precise r...

Multidimensional correlates of psychological stress: Insights from traditional statistical approaches and machine learning using a nationally representative Canadian sample.

Approximately one-fifth of Canadians report high levels of psychological stress. This is alarming, as chronic stress is associated with non-communicab...

A Machine Learning Model Using Cardiac CT and MRI Data Predicts Cardiovascular Events in Obstructive Coronary Artery Disease.

Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning ...

A Data-Centric Approach to Detecting and Mitigating Demographic Bias in Pediatric Mental Health Text: A Case Study in Anxiety Detection

Introduction: Healthcare AI models often inherit biases from their training data. While efforts have primarily targeted bias in structured data, men...

Depression and Anxiety Prediction Using Deep Language Models and Transfer Learning

Digital screening and monitoring applications can aid providers in the management of behavioral health conditions. We explore deep language models f...

[Optimization of centrifugal artificial heart pump blade parameters based on back propagation neural network and grey wolf optimization algorithm].

The impeller, as a key component of artificial heart pumps, experiences high shear stress due to its rapid rotation, which may lead to hemolysis. To e...

Detecting anxiety and depression in dialogues: a multi-label and explainable approach

Anxiety and depression are the most common mental health issues worldwide, affecting a non-negligible part of the population. Accordingly, stakehold...

Modeling Battery Electric Vehicle Users' Charging Decisions in Scenarios with Both Time-Related and Distance-Related Anxiety

As one of the most promising alternatives to internal combustion engine vehicles, battery electric vehicles (BEVs) have become increasingly prevalen...

Cognition Chain for Explainable Psychological Stress Detection on Social Media

Stress is a pervasive global health issue that can lead to severe mental health problems. Early detection offers timely intervention and prevention ...

Artificial Intelligence in Mental Health and Well-Being: Evolution, Current Applications, Future Challenges, and Emerging Evidence

Artificial Intelligence (AI) is a broad field that is upturning mental health care in many ways, from addressing anxiety, depression, and stress to ...

Machine Learning Algorithms for Detecting Mental Stress in College Students

In today's world, stress is a big problem that affects people's health and happiness. More and more people are feeling stressed out, which can lead ...

Exploring Complex Mental Health Symptoms via Classifying Social Media Data with Explainable LLMs

We propose a pipeline for gaining insights into complex diseases by training LLMs on challenging social media text data classification tasks, obtain...

Real-Time Prediction for Athletes' Psychological States Using BERT-XGBoost: Enhancing Human-Computer Interaction

Understanding and predicting athletes' mental states is crucial for optimizing sports performance. This study introduces a hybrid BERT-XGBoost model...

RiceSNP-ABST: a deep learning approach to identify abiotic stress-associated single nucleotide polymorphisms in rice.

Given the adverse effects faced by rice due to abiotic stresses, the precise and rapid identification of single nucleotide polymorphisms (SNPs) associ...

Identification and validation of oxidative stress-related genes in primary open-angle glaucoma by weighted gene co-expression network analysis and machine learning.

Primary open-angle glaucoma (POAG) is a common ocular disease, and there is currently no effective treatment for POAG therapy. Thus, identifying some ...

PhDGPT: Introducing a psychometric and linguistic dataset about how large language models perceive graduate students and professors in psychology

Machine psychology aims to reconstruct the mindset of Large Language Models (LLMs), i.e. how these artificial intelligences perceive and associate i...

Electromechanical Dynamics of the Heart: A Study of Cardiac Hysteresis During Physical Stress Test

Cardiovascular diseases are best diagnosed using multiple modalities that assess both the heart's electrical and mechanical functions. While effecti...

AI-Driven Early Mental Health Screening: Analyzing Selfies of Pregnant Women

Major Depressive Disorder and anxiety disorders affect millions globally, contributing significantly to the burden of mental health issues. Early sc...