AIMC Topic: Stress, Psychological
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Factors influencing psychological distress among breast cancer survivors using machine learning techniques.
Scientific reports
Jul 1, 2024
Breast cancer is the most commonly diagnosed cancer among women worldwide. Breast cancer patients experience significant distress relating to their diagnosis and treatment. Managing this distress is critical for improving the lifespan and quality of ...
A Novel AI Approach for Assessing Stress Levels in Patients with Type 2 Diabetes Mellitus Based on the Acquisition of Physiological Parameters Acquired during Daily Life.
Sensors (Basel, Switzerland)
Jun 27, 2024
Stress is the inherent sensation of being unable to handle demands and occurrences. If not properly managed, stress can develop into a chronic condition, leading to the onset of additional chronic health issues, such as cardiovascular illnesses and d...
Predictive modelling of stress, anxiety and depression: A network analysis and machine learning study.
The British journal of clinical psychology
Jun 25, 2024
OBJECTIVE: This study assessed predictors of stress, anxiety and depression during the COVID-19 pandemic using a large number of demographic, COVID-19 context and psychological variables.
Black-white differences in chronic stress exposures to predict preterm birth: interpretable, race/ethnicity-specific machine learning model.
BMC pregnancy and childbirth
Jun 22, 2024
BACKGROUND: Differential exposure to chronic stressors by race/ethnicity may help explain Black-White inequalities in rates of preterm birth. However, researchers have not investigated the cumulative, interactive, and population-specific nature of ch...
Robots as Mental Health Coaches: A Study of Emotional Responses to Technology-Assisted Stress Management Tasks Using Physiological Signals.
Sensors (Basel, Switzerland)
Jun 21, 2024
The current study investigated the effectiveness of social robots in facilitating stress management interventions for university students by evaluating their physiological responses. We collected electroencephalogram (EEG) brain activity and Galvanic...
Machine learning identifies different related factors associated with depression and suicidal ideation in Chinese children and adolescents.
Journal of affective disorders
Jun 4, 2024
BACKGROUND: Depression and suicidal ideation often co-occur in children and adolescents, yet they possess distinct characteristics. This study sought to identify the different related factors associated with depression and suicidal ideation.
Accelerated construction of stress relief music datasets using CNN and the Mel-scaled spectrogram.
PloS one
May 24, 2024
Listening to music is a crucial tool for relieving stress and promoting relaxation. However, the limited options available for stress-relief music do not cater to individual preferences, compromising its effectiveness. Traditional methods of curating...
Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features.
Sensors (Basel, Switzerland)
May 18, 2024
Stress recognition, particularly using machine learning (ML) with physiological data such as heart rate variability (HRV), holds promise for mental health interventions. However, limited datasets in affective computing and healthcare research can lea...
Deciphering the Genetic Links between Psychological Stress, Autophagy, and Dermatological Health: Insights from Bioinformatics, Single-Cell Analysis, and Machine Learning in Psoriasis and Anxiety Disorders.
International journal of molecular sciences
May 15, 2024
The relationship between psychological stress, altered skin immunity, and autophagy-related genes (ATGs) is currently unclear. Psoriasis is a chronic skin inflammation of unclear etiology that is characterized by persistence and recurrence. Immune dy...
A machine-learning approach to model risk and protective factors of vulnerability to depression.
Journal of psychiatric research
May 11, 2024
There are multiple risk and protective factors for depression. The association between these factors with vulnerability to depression is unclear. Such knowledge is an important insight into assessing risk for developing depression for precision inter...
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