Application of artificial intelligence on psychological interventions and diagnosis: an overview

S Zhou, J Zhao, L Zhang - Frontiers in Psychiatry, 2022 - frontiersin.org
Background Innovative technologies, such as machine learning, big data, and artificial
intelligence (AI) are approaches adopted for personalized medicine, and psychological …

End-to-end multimodal clinical depression recognition using deep neural networks: A comparative analysis

M Muzammel, H Salam, A Othmani - Computer Methods and Programs in …, 2021 - Elsevier
Abstract Background and Objective: Major Depressive Disorder is a highly prevalent and
disabling mental health condition. Numerous studies explored multimodal fusion systems …

Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence

MD Nemesure, MV Heinz, R Huang, NC Jacobson - Scientific reports, 2021 - nature.com
Generalized anxiety disorder (GAD) and major depressive disorder (MDD) are highly
prevalent and impairing problems, but frequently go undetected, leading to substantial …

Monitoring changes in depression severity using wearable and mobile sensors

P Pedrelli, S Fedor, A Ghandeharioun, E Howe… - Frontiers in …, 2020 - frontiersin.org
Background: While preliminary evidence suggests that sensors may be employed to detect
presence of low mood it is still unclear whether they can be leveraged for measuring …

DCTNet: hybrid deep neural network-based EEG signal for detecting depression

Y Chen, S Wang, J Guo - Multimedia Tools and Applications, 2023 - Springer
Depression is a mood disorder that can affect people's psychological problems. The current
medical approach is to detect depression by manual analysis of EEG signals, however …

Unravelling the complexities of depression with medical intelligence: exploring the interplay of genetics, hormones, and brain function

MBB Heyat, F Akhtar, F Munir, A Sultana… - Complex & Intelligent …, 2024 - Springer
Depression is a multifactorial disease with unknown etiology affecting globally. It's the
second most significant reason for infirmity in 2020, affecting about 50 million people …

[HTML][HTML] An in-depth analysis of machine learning approaches to predict depression

MS Zulfiker, N Kabir, AA Biswas, T Nazneen… - Current research in …, 2021 - Elsevier
Among all the forms of psychological and mental disorders, depression is the most common
form. Nowadays a large number of youths and adults around the world suffer from …

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Y Kumar, A Koul, R Singla, MF Ijaz - Journal of ambient intelligence and …, 2023 - Springer
Artificial intelligence can assist providers in a variety of patient care and intelligent health
systems. Artificial intelligence techniques ranging from machine learning to deep learning …

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 …

Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis

A Khosla, P Khandnor, T Chand - Biocybernetics and Biomedical …, 2022 - Elsevier
Depression is one of the significant contributors to the global burden disease, affecting
nearly 264 million people worldwide along with the increasing rate of suicidal deaths …