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eRisk 2023: Depression, Pathological Gambling, 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 Tendency Assessment Based on Cyber Psychosocial and Physical Computation
In modern social environment, depression tendency has become a common psychological state which can cause a variety of adverse effects on people’s...
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Explainable cross-lingual depression identification based on multi-head attention networks in Thai context
Depression is a significant global mental health challenge, and its early detection is crucial for effective treatment. Social media platforms are...
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MMDA: A Multimodal Dataset for Depression and Anxiety Detection
In recent years, with the development of artificial intelligence, the use of machine learning and other methods for mental illness detection has...
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OCWYOLO: A Road Depression Detection Method
In terms of road depression detection, there are four existing methods: (1) based on two-dimensional image processing methods, (2) three-dimensional...
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Highest Accuracy Based Automated Depression Prediction Using Natural Language Processing
In today's fast-paced society, psychological health issues such as anxiety, depression, and stress have become prevalent among the general...
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Utilizing Chatbots as Predictive Tools for Anxiety and Depression: A Bibliometric Review
This article addresses the impact of the implementation of medical chatbots as a tool to predict mental health disorders on society, focusing on the...
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An Intelligent Mobile System for Monitoring Relapse of Depression
Depression is a common psychological disorder with high relapse rate in modern society. Due to weak self-perception and fear of public bias, most...
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Depression Detection Using Deep Learning and Natural Language Processing Techniques: A Comparative Study
Depression is a frequently underestimated illness that significantly impacts a substantial number of individuals worldwide, making it a significant...
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AB-BiL: A Deep Learning Model to Analyze Depression Detection in Imbalanced Data
The usage of online resources (websites, social media, blogs, etc.) to express personal opinions is increasing daily. Annually, there are...
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Towards automatic text-based estimation of depression through symptom prediction
Major Depressive Disorder (MDD) is one of the most common and comorbid mental disorders that impacts a person’s day-to-day activity. In addition, MDD...
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PsyProf: A Platform for Assisted Screening of Depression in Social Media
Depression is one of the most prevalent mental disorders. For its effective treatment, patients need a quick and accurate diagnosis. Mental health...
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Depression detection via a Chinese social media platform: a novel causal relation-aware deep learning approach
Depression detection on social media aims to analyze users’ tendency to depression and provide help for the early detection of depressed users....
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Early detection of depression through facial expression recognition and electroencephalogram-based artificial intelligence-assisted graphical user interface
Psychological disorders have increased globally at an alarming rate. Among these disorders, depression stands out as one of the leading and most...
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DCTNet: hybrid deep neural network-based EEG signal for detecting depression
Depression is a mood disorder that can affect people’s psychological problems. The current medical approach is to detect depression by manual...
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Depression clinical detection model based on social media: a federated deep learning approach
Depression can significantly impact people’s mental health, and recent research shows that social media can provide decision-making support for...
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An Automatic Depression Detection Method with Cross-Modal Fusion Network and Multi-head Attention Mechanism
Audio-visual based multimodal depression detection has gained significant attention due to its high efficiency and convenience as a computer-aided...
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A decision integration strategy algorithm to detect the depression severity level using wearable and profile data
Mental health issues are becoming more common, and they are often experienced by people who would rather live alone, spend a lot of time on social...
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Predictive Model for Depression and Anxiety Using Machine Learning Algorithms
Psychological health disorders have grown quite widespread in recent decades. In this work, a few machine learning algorithms were used to predict...
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Assessing Depression Health Information Using Machine Learning
Evaluating health information using machine learning is a must, especially with the tremendous growth of internet resources. Increased usage of...