AIMC Topic: Depression
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Relationship matters: Using machine learning methods to predict the mental health severity of Chinese college freshmen during the pandemic period.
Journal of affective disorders
Sep 26, 2024
BACKGROUND: Pandemics act as stressors and may lead to frequent mental health disorders. College student, especially freshmen, are particularly susceptible to experiencing intense mental stress reactions during a pandemic. We aimed to identify stable...
A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly.
Journal of affective disorders
Sep 24, 2024
BACKGROUND: With the continuous advancement of age in China, attention should be paid to the mental well-being of the elderly population. The present study uses a novel machine learning (ML) method on a large representative elderly database in China ...
Application Value of a Machine Learning Model in Predicting Mild Depression Associated with Migraine without Aura.
British journal of hospital medicine (London, England : 2005)
Sep 19, 2024
To investigate the application value of a machine learning model in predicting mild depression associated with migraine without aura (MwoA). 178 patients with MwoA admitted to the Department of Neurology of the First Affiliated Hospital of Anhui Un...
Deep Learning-Based Artificial Intelligence Can Differentiate Treatment-Resistant and Responsive Depression Cases with High Accuracy.
Clinical EEG and neuroscience
Sep 9, 2024
Although there are many treatment options available for depression, a large portion of patients with depression are diagnosed with treatment-resistant depression (TRD), which is characterized by an inadequate response to antidepressant treatment. Id...
Prediction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets.
EBioMedicine
Sep 9, 2024
BACKGROUND: Depressive symptoms are rising in the general population, but their associated factors are unclear. Although the link between sleep disturbances and depressive symptoms severity (DSS) is reported, the predictive role of sleep on DSS and t...
Risk factors for depression in China based on machine learning algorithms: A cross-sectional survey of 264,557 non-manual workers.
Journal of affective disorders
Sep 5, 2024
BACKGROUND: Factors related to depression differ depending on the population studied, and studies focusing on the population of non-manual workers are lacking. Thus, we aimed to identify the risk factors related to depression in non-manual workers in...
HEMAsNet: A Hemisphere Asymmetry Network Inspired by the Brain for Depression Recognition From Electroencephalogram Signals.
IEEE journal of biomedical and health informatics
Sep 5, 2024
Depression is a prevalent mental disorder that affects a significant portion of the global population. Despite recent advancements in EEG-based depression recognition models rooted in machine learning and deep learning approaches, many lack comprehen...
EEGDepressionNet: A Novel Self Attention-Based Gated DenseNet With Hybrid Heuristic Adopted Mental Depression Detection Model Using EEG Signals.
IEEE journal of biomedical and health informatics
Sep 5, 2024
World Health Organization (WHO) has identified depression as a significant contributor to global disability, creating a complex thread in both public and private health. Electroencephalogram (EEG) can accurately reveal the working condition of the hu...
Natural Language Processing for Depression Prediction on Sina Weibo: Method Study and Analysis.
JMIR mental health
Sep 4, 2024
BACKGROUND: Depression represents a pressing global public health concern, impacting the physical and mental well-being of hundreds of millions worldwide. Notwithstanding advances in clinical practice, an alarming number of individuals at risk for de...
Using machine learning models to identify the risk of depression in middle-aged and older adults with frequent and infrequent nicotine use: A cross-sectional study.
Journal of affective disorders
Aug 31, 2024
BACKGROUND: Depression is very prevalent in middle-aged and older smokers. Therefore, we aimed to identify the risk of depression among middle-aged and older adults with frequent and infrequent nicotine use, as this is quite necessary for supporting ...
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