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 …

Deep learning for depression recognition with audiovisual cues: A review

L He, M Niu, P Tiwari, P Marttinen, R Su, J Jiang… - Information …, 2022 - Elsevier
With the acceleration of the pace of work and life, people are facing more and more
pressure, which increases the probability of suffering from depression. However, many …

Machine learning for multimodal mental health detection: a systematic review of passive sensing approaches

LS Khoo, MK Lim, CY Chong, R McNaney - Sensors, 2024 - mdpi.com
As mental health (MH) disorders become increasingly prevalent, their multifaceted
symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing …

Automatic depression recognition by intelligent speech signal processing: A systematic survey

P Wu, R Wang, H Lin, F Zhang, J Tu… - CAAI Transactions on …, 2023 - Wiley Online Library
Depression has become one of the most common mental illnesses in the world. For better
prediction and diagnosis, methods of automatic depression recognition based on speech …

AVEC 2018 workshop and challenge: Bipolar disorder and cross-cultural affect recognition

F Ringeval, B Schuller, M Valstar, R Cowie… - Proceedings of the …, 2018 - dl.acm.org
The Audio/Visual Emotion Challenge and Workshop (AVEC 2018)" Bipolar disorder, and
cross-cultural affect recognition''is the eighth competition event aimed at the comparison of …

Augmented datasheets for speech datasets and ethical decision-making

O Papakyriakopoulos, ASG Choi, W Thong… - Proceedings of the …, 2023 - dl.acm.org
Speech datasets are crucial for training Speech Language Technologies (SLT); however,
the lack of diversity of the underlying training data can lead to serious limitations in building …

Multimodal deep learning framework for mental disorder recognition

Z Zhang, W Lin, M Liu… - 2020 15th IEEE …, 2020 - ieeexplore.ieee.org
Current methods for mental disorder recognition mostly depend on clinical interviews and
self-reported scores that can be highly subjective. Building an automatic recognition system …

Multimodal temporal machine learning for Bipolar Disorder and Depression Recognition

F Ceccarelli, M Mahmoud - Pattern Analysis and Applications, 2022 - Springer
Mental disorder is a serious public health concern that affects the life of millions of people
throughout the world. Early diagnosis is essential to ensure timely treatment and to improve …

A multimodal approach for mania level prediction in bipolar disorder

P Baki, H Kaya, E Çiftçi, H Güleç… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Bipolar disorder is a mental health disorder that causes mood swings that range from
depression to mania. Clinical diagnosis of bipolar disorder is based on patient interviews …

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 …