[HTML][HTML] Natural language processing applied to mental illness detection: a narrative review

T Zhang, AM Schoene, S Ji, S Ananiadou - NPJ digital medicine, 2022 - nature.com
Mental illness is highly prevalent nowadays, constituting a major cause of distress in
people's life with impact on society's health and well-being. Mental illness is a complex multi …

Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A …

S Yasin, A Othmani, I Raza, SA Hussain - Computers in Biology and …, 2023 - Elsevier
Mental disorders are rapidly increasing each year and have become a major challenge
affecting the social and financial well-being of individuals. There is a need for phenotypic …

Depression detection based on hybrid deep learning SSCL framework using self-attention mechanism: An application to social networking data

A Nadeem, M Naveed, M Islam Satti, H Afzal, T Ahmad… - Sensors, 2022 - mdpi.com
In today's world, mental health diseases have become highly prevalent, and depression is
one of the mental health problems that has become widespread. According to WHO reports …

Speechformer++: A hierarchical efficient framework for paralinguistic speech processing

W Chen, X Xing, X Xu, J Pang… - IEEE/ACM Transactions …, 2023 - ieeexplore.ieee.org
Paralinguistic speech processing is important in addressing many issues, such as sentiment
and neurocognitive disorder analyses. Recently, Transformer has achieved remarkable …

SpeechFormer: A hierarchical efficient framework incorporating the characteristics of speech

W Chen, X Xing, X Xu, J Pang, L Du - arXiv preprint arXiv:2203.03812, 2022 - arxiv.org
Transformer has obtained promising results on cognitive speech signal processing field,
which is of interest in various applications ranging from emotion to neurocognitive disorder …

Multimodal sensing for depression risk detection: integrating audio, video, and text data

Z Zhang, S Zhang, D Ni, Z Wei, K Yang, S Jin, G Huang… - Sensors, 2024 - mdpi.com
Depression is a major psychological disorder with a growing impact worldwide. Traditional
methods for detecting the risk of depression, predominantly reliant on psychiatric …

Automatic depression detection via learning and fusing features from visual cues

Y Guo, C Zhu, S Hao, R Hong - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Depression is one of the most prevalent mental disorders, which seriously affects one's life.
Traditional depression diagnostics commonly depend on rating with scales, which can be …

D-ResNet-PVKELM: deep neural network and paragraph vector based kernel extreme machine learning model for multimodal depression analysis

SJ TJ, IJ Jacob, AK Mandava - Multimedia Tools and Applications, 2023 - Springer
Nowadays, depression heavily affects humans' physical and mental health. Depression
occurs due to changes in mood, loss of interest, and stress, which leads to self-harm events …

Using graph representation learning with schema encoders to measure the severity of depressive symptoms

S Hong, A Cohn, D Hogg - The Tenth International …, 2022 - eprints.whiterose.ac.uk
Graph neural networks (GNNs) are widely used in regression and classification problems
applied to text, in areas such as sentiment analysis and medical decision-making processes …

Review and content analysis of textual expressions as a marker for depressive and anxiety disorders (DAD) detection using machine learning

CM Sharma, D Damani, VM Chariar - Discover Artificial Intelligence, 2023 - Springer
Depressive disorders (including major depressive disorder and dysthymia) and anxiety
(generalized anxiety disorder or GAD) disorders are the two most prevalent mental illnesses …