[HTML][HTML] A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges

A Montejo-Ráez, MD Molina-González… - Computer Science …, 2024 - Elsevier
For years, the scientific community has researched monitoring approaches for the detection
of certain mental disorders and risky behaviors, like depression, eating disorders, gambling …

A multimodal fusion model with multi-level attention mechanism for depression detection

M Fang, S Peng, Y Liang, CC Hung, S Liu - Biomedical Signal Processing …, 2023 - Elsevier
Depression is a common mental illness that affects the physical and mental health of
hundreds of millions of people around the world. Therefore, designing an efficient and …

[PDF][PDF] Improving depression prediction accuracy using fisher score-based feature selection and dynamic ensemble selection approach based on acoustic features of …

N Janardhan, N Kumaresh - Traitement du Signal, 2022 - researchgate.net
Accepted: 13 February 2022 Depression affects over 322 million people, and it is the most
common source of disability worldwide. Literature in speech processing revealed that …

Trial selection tensor canonical correlation analysis (TSTCCA) for depression recognition with facial expression and pupil diameter

M Yang, Y Wu, Y Tao, X Hu, B Hu - IEEE Journal of Biomedical …, 2023 - ieeexplore.ieee.org
Facial expressions have been widely used for depression recognition because it is intuitive
and convenient to access. Pupil diameter contains rich emotional information that is already …

[HTML][HTML] Depression recognition using a proposed speech chain model fusing speech production and perception features

M Du, S Liu, T Wang, W Zhang, Y Ke, L Chen… - Journal of Affective …, 2023 - Elsevier
Background Increasing depression patients puts great pressure on clinical diagnosis. Audio-
based diagnosis is a helpful auxiliary tool for early mass screening. However, current …

Towards automatic text-based estimation of depression through symptom prediction

K Milintsevich, K Sirts, G Dias - Brain Informatics, 2023 - Springer
Abstract 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 affects one's linguistic …

Automatic depression severity assessment with deep learning using parameter-efficient tuning

C Lau, X Zhu, WY Chan - Frontiers in Psychiatry, 2023 - frontiersin.org
Introduction To assist mental health care providers with the assessment of depression,
research to develop a standardized, accessible, and non-invasive technique has garnered …

A hybrid feature selection and ensemble approach to identify depressed users in online social media

J Liu, M Shi - Frontiers in Psychology, 2022 - frontiersin.org
Depression has become one of the most common mental illnesses, and the widespread use
of social media provides new ideas for detecting various mental illnesses. The purpose of …

Three-stream convolutional neural network for depression detection with ocular imaging

M Yang, Z Weng, Y Zhang, Y Tao… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Depression is a prevalent and severe mental disorder that significantly affects both mind and
body, leading to persistent feelings of sadness, despair, and impaired functionality …

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 …