A review of machine learning and deep learning approaches on mental health diagnosis

NK Iyortsuun, SH Kim, M Jhon, HJ Yang, S Pant - Healthcare, 2023 - mdpi.com
Combating mental illnesses such as depression and anxiety has become a global concern.
As a result of the necessity for finding effective ways to battle these problems, machine …

Natural language processing for mental health interventions: a systematic review and research framework

M Malgaroli, TD Hull, JM Zech, T Althoff - Translational Psychiatry, 2023 - nature.com
Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack
of objective outcomes and fidelity metrics. AI technologies and specifically Natural …

Depression detection from social networks data based on machine learning and deep learning techniques: An interrogative survey

KM Hasib, MR Islam, S Sakib, MA Akbar… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Users can interact with one another through social networks (SNs) by exchanging
information, delivering comments, finding new information, and engaging in discussions that …

Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media

H Zogan, I Razzak, X Wang, S Jameel, G Xu - World Wide Web, 2022 - Springer
The ability to explain why the model produced results in such a way is an important problem,
especially in the medical domain. Model explainability is important for building trust by …

An hybrid deep learning approach for depression prediction from user tweets using feature-rich CNN and bi-directional LSTM

H Kour, MK Gupta - Multimedia Tools and Applications, 2022 - Springer
Depression has become one of the most widespread mental health disorders across the
globe. Depression is a state of mind which affects how we think, feel, and act. The number of …

Depression detection in clinical interviews with LLM-empowered structural element graph

Z Chen, J Deng, J Zhou, J Wu, T Qian… - Proceedings of the …, 2024 - aclanthology.org
Depression is a widespread mental health disorder affecting millions globally. Clinical
interviews are the gold standard for assessing depression, but they heavily rely on scarce …

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 …

A hybrid model for depression detection with transformer and bi-directional long short-term memory

Y Zhang, Y He, L Rong, Y Ding - 2022 IEEE international …, 2022 - ieeexplore.ieee.org
Failure to diagnose and treat depression in a timely manner causes more than three
hundred million people suffering from this mental health disorder worldwide. Depression, a …

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 …

Measuring non-typical emotions for mental health: A survey of computational approaches

P Kumar, A Vedernikov, X Li - arxiv preprint arxiv:2403.08824, 2024 - arxiv.org
Analysis of non-typical emotions, such as stress, depression and engagement is less
common and more complex compared to that of frequently discussed emotions like …