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Co** with Depression
Depression is a severe mood disorder that is frequent in the population and can negatively affect several aspects of life. This article summarizes...
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Multi-source Information Fusion for Depression Detection
Depression is the most common psychiatric disorder. Traditional depression detection methods almost rely on structured scales and clinical opinions,...
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Detection of Depression Symptoms Through Unsupervised Learning
An increase in the decline of mental health in student populations has been observed since 2019. The objective of this study is to characterize the...
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Item-Specific Similarity Assessments for Explainable Depression Screening
Depression, a prevalent mental health issue worldwide, is deeply influenced by the cultural and sociodemographic context, particularly in the Yucatán...
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Detect Depression from Social Networks with Sentiment Knowledge Sharing
Social network plays an important role in propagating people’s viewpoints, emotions, thoughts, and fears. Notably, following lockdown periods during...
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DepBoost-TransNet: Boosted Transformer Network for Depression Classification
Depression presents a significant global mental health challenge affecting countless individuals across the globe. Early detection is of paramount...
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Multimodal Depression Recognition Using Audio and Visual
Depression, as one of the prominent challenges in the field of worldwide psychological health, affects the quality of life and psychological...
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Predictive Modeling for Detection of Depression Using Machine Learning
Predictive modeling techniques using artificial intelligence have shown promising potential in detecting and predicting depression in recent times,...
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eRisk 2024: Depression, Anorexia, and Eating Disorder Challenges
In 2017, we launched eRisk as a CLEF Lab to encourage research on early risk detection on the Internet. Since then, thanks to the participants’ work,...
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Depression Detection Using Distribution of Microstructures from Actigraph Information
Depression negatively affects the daily life of an individual and may even lead to suicidal tendencies. The problem is compounded by the scarcity of...
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CANAMRF: An Attention-Based Model for Multimodal Depression Detection
Multimodal depression detection is an important research topic that aims to predict human mental states using multimodal data. Previous methods treat...
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Early Detection of Depression and Alcoholism Disorders by EEG Signal
The World Health Organization reported that more than 264 and 80 million patients worldwide suffer from depression and alcoholism, respectively....
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Depression detection via conversation turn classification
The fast pace of modern life caused people to experience more pressure from their surrounding environments. As a result, depression has emerged as...
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An Audio Correlation-Based Graph Neural Network for Depression Recognition
Depression is a prevalent mental health disorder. The diagnosis of depression hinges largely on the medical practitioner’s subjective assessment of...
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SEOE: an option graph based semantically embedding method for prenatal depression detection
Prenatal depression, which can affect pregnant women’s physical and psychological health and cause postpartum depression, is increasing dramatically....
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Adaptive Neuro Fuzzy-Based Depression Detection Model for Students in Tertiary Education
Depression is a severe mental disorder with characteristic symptoms such as sadness, feeling of emptiness, anger, anxiety and sleep disturbance as...
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Deep Depression Detection Based on Feature Fusion and Result Fusion
Depression, as a severe mental disorder, has significant impacts on individuals, families, and society. Accurate depression detection is of great...
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From Words to Emotions: Identifying Depression Through Social Media Insights
The rise of social media has led to a drastic surge in the dissemination of hostile and toxic content, fostering an alarming proliferation of hate...
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A depression detection model based on multimodal graph neural network
Depression is a prevalent mental illness, especially major depression, which has a negative impact on individuals and society. In clinical practice,...
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Rhythm Formant Analysis for Automatic Depression Classification
This paper presents a study on the application of Rhythm Formant Analysis (RFA) for automatic depression classification in speech signals. The...