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Depression

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Tai Chi Movement Recognition and Precise Intervention for the Elderly Based on Inertial Measurement Units and Temporal Convolutional Neural Networks.

Sensors (Basel, Switzerland)
(1) Background: The objective of this study was to recognize tai chi movements using inertial measurement units (IMUs) and temporal convolutional neural networks (TCNs) and to provide precise interventions for elderly people. (2) Methods: This study ...

Multilayer Perceptron-Based Wearable Exercise-Related Heart Rate Variability Predicts Anxiety and Depression in College Students.

Sensors (Basel, Switzerland)
(1) Background: This study aims to investigate the correlation between heart rate variability (HRV) during exercise and recovery periods and the levels of anxiety and depression among college students. Additionally, the study assesses the accuracy of...

Predictive modelling of stress, anxiety and depression: A network analysis and machine learning study.

The British journal of clinical psychology
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.

Predicting Depression, Anxiety, and Their Comorbidity among Patients with Breast Cancer in China Using Machine Learning: A Multisite Cross-Sectional Study.

Depression and anxiety
Depression and anxiety are highly prevalent among patients with breast cancer. We tested the capacity of personal resources (psychological resilience, social support, and process of recovery) for predicting depression, anxiety, and comorbid depressio...

Individual Predictors of Response to A Behavioral Activation-Based Digital Smoking Cessation Intervention: A Machine Learning Approach.

Substance use & misuse
Depression is prevalent among individuals who smoke cigarettes and increases risk for relapse. A previous clinical trial suggests that Goal2Quit, a behavioral activation-based smoking cessation mobile app, effectively increases smoking abstinence an...

Brain-computer interfaces inspired spiking neural network model for depression stage identification.

Journal of neuroscience methods
BACKGROUND: Depression is a global mental disorder, and traditional diagnostic methods mainly rely on scales and subjective evaluations by doctors, which cannot effectively identify symptoms and even carry the risk of misdiagnosis. Brain-Computer Int...

Mental health analysis of international students using machine learning techniques.

PloS one
International students' mental health has become an increasing concern in recent years, as more students leave their country for better education. They experience a wide range of challenges while studying abroad that have an impact on their psycholog...

Identifying the risk of depression in a large sample of adolescents: An artificial neural network based on random forest.

Journal of adolescence
BACKGROUND: This study aims to develop an artificial neural network (ANN) prediction model incorporating random forest (RF) screening ability for predicting the risk of depression in adolescents and identifies key risk factors to provide a new approa...

Machine learning identifies different related factors associated with depression and suicidal ideation in Chinese children and adolescents.

Journal of affective disorders
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.

Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms.

BMC medical research methodology
In contemporary society, depression has emerged as a prominent mental disorder that exhibits exponential growth and exerts a substantial influence on premature mortality. Although numerous research applied machine learning methods to forecast signs o...