Methods for Assessing Mood Changes in Remote Learning With Deep Learning Techniques
Abstract
The burden of mental disorders continues to grow and has a marked impact on health systems around the world. It has serious social and economic consequences. Unlike physical illnesses, mental health problems are often overlooked. It is very important to get a diagnosis and timely treatment before it can become serious. However, current diagnostic methods are based on subjective assessments by experts, making treatment difficult and costly. This article provides an overview of artificial intelligence (AI) technology and its applications in health care, a review of recent original AI research on mental health, and a discussion of how AI can complement the current distance learning model aimed at monitoring the emotional state of learners, as well as areas for additional research. Several studies were reviewed that used videos captured with various devices and pictures to predict and classify mental illnesses including depression, mood disorders (affective disorders) and others as well as different levels of stress. Collectively, these studies have shown high accuracy and have provided excellent examples of AI's potential in the mental health field. Most of these should be seen as early trial work demonstrating the potential of using machine learning algorithms to address mental health problems. However, caution is necessary in order to avoid misinterpreting preliminary results and not to violate ethical boundaries.
References
2. Taquet M., Geddes J.R., Husain M., Luciano S., Harrison P.J. 6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: a retrospective cohort study using electronic health records. The Lancet. Psychiatry. 2021; 8(5):416-427. (In Eng.) DOI: https://doi.org/10.1016/S2215-0366(21)00084-5
3. Brooks S.K., Webster R.K., Smith L.E., Woodland L., Wessely S., The psychological impact of quarantine and how to reduce it: rapid review of the evidence. The Lancet. 2020; 395(10227):912-920. (In Eng.) DOI: https://doi.org/10.1016/S0140-6736(20)30460-8
4. Lischer S., Safi N., Dickson C. Remote learning and students’ mental health during the Covid-19 pandemic: A mixed-method enquiry. PROSPECTS. 2021. p. 1-11. (In Eng.) DOI: https://doi.org/10.1007/s11125-020-09530-w
5. Durstewitz D., Koppe G., Meyer-Lindenberg A. Deep neural networks in psychiatry. Molecular Psychiatry. 2019; 24:1583-1598. (In Eng.) DOI: https://doi.org/10.1038/s41380-019-0365-9
6. Dwyer D.B., Falkai P., Koutsouleris N. Machine Learning Approaches for Clinical Psychology and Psychiatry. Annual Review of Clinical Psychology. 2018; 14:91-118. (In Eng.) DOI: https://doi.org/10.1146/annurev-clinpsy-032816-045037
7. Lovejoy C.A., Buch V., Maruthappu M. Technology and mental health: The role of artificial intelligence. European Psychiatry. 2019; 55:1-3. (In Eng.) DOI: https://doi.org/10.1016/j.eurpsy.2018.08.004
8. Shatte A.B.R., Hutchinson D.M., Teague S.J. Machine learning in mental health: a scoping review of methods and applications. Psychological Medicine. 2019; 49(9):1426-1448. (In Eng.) DOI: https://doi.org/10.1017/S0033291719000151
9. Miotto R., Wang F., Wang S., Jiang X., Dudley J.T. Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics. 2017; 19(6):1236-1246. (In Eng.) DOI: https://doi.org/10.1093/bib/bbx044
10. Giannakakis G., Pediaditis M., Manousos D., Kazantzaki E., Chiarugi F., Simos P.G., Marias K., Tsiknakis M. Stress and anxiety detection using facial cues from videos. Biomedical Signal Processing and Control. 2017; 31:89-101. (In Eng.) DOI: https://doi.org/10.1016/j.bspc.2016.06.020
11. Miotto R., Wang F., Wang S., Jiang X., Dudley J.T. Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics. 2017; 19(6):1236-1246. (In Eng.) DOI: https://doi.org/10.1093/bib/bbx044
12. Shickel B., Tighe P.J., Bihorac A., Rashidi P. Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis. IEEE Journal of Biomedical and Health Informatics. 2018; 22(5):1589-1604. (In Eng.) DOI: https://doi.org/10.1109/JBHI.2017.2767063
13. Calvo R.A., Milne D.N., Hussain M.S., Christensen H. Natural language processing in mental health applications using non-clinical texts. Natural Language Engineering. 2017; 23(5):649-685. (In Eng.) DOI: https://doi.org/10.1017/S1351324916000383
14. Murphy K.P. Machine Learning: A Probabilistic Perspective. MIT Press, Cambridge, Massachusetts; 2012. 1104 p. (In Eng.)
15. Lecun Y., Bottou L., Bengio Y., Haffner P. Gradient-based learning applied to document recognition Proceedings of the IEEE. 1998; 86(11):2278-2324. (In Eng.) DOI: https://doi.org/10.1109/5.726791
16. Liou C., Cheng W., Liou J., Liou D. Autoencoder for words. Neurocomputing. 2014; 139:84-96. (In Eng.) DOI: https://doi.org/10.1016/j.neucom.2013.09.055
17. Jaiswal S., Valstar M.F., Gillott A., Daley D. Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data. 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017). IEEE Press, Washington, DC, USA; 2017. p. 762-769. (In Eng.) DOI: https://doi.org/10.1109/FG.2017.95
18. Su M.H., Wu C.H., Huang K.Y., Hong Q.B., Wang H.M. Exploring microscopic fluctuation of facial expression for mood disorder classification. 2017 International Conference on Orange Technologies (ICOT). IEEE Press, Singapore; 2017. p. 65-69. (In Eng.) DOI: https://doi.org/10.1109/ICOT.2017.8336090
19. Dawood A., Turner S., Perepa P. Affective Computational Model to Extract Natural Affective States of Students With Asperger Syndrome (AS) in Computer-Based Learning Environment. IEEE Access. 2018; 6:67026-67034. (In Eng.) DOI: https://doi.org/10.1109/ACCESS.2018.2879619
20. Prasetio B.H., Tamura H., Tanno K. The Facial Stress Recognition Based on Multi-histogram Features and Convolutional Neural Network. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE Press, Miyazaki, Japan; 2018. p. 881-887. (In Eng.) DOI: https://doi.org/10.1109/SMC.2018.00157
21. Zhang H., Feng L., Li N,. Jin Z., Cao L. Video-based stress detection through deep learning. Sensors. 2020; 20(19):5552. (In Eng.) DOI: https://doi.org/10.3390/s20195552
22. Janssen R.J., Mourão-Miranda J., Schnack H.G. Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2018; 3(9):798-808. (In Eng.) DOI: https://doi.org/10.1016/j.bpsc.2018.04.004
23. Luxton D.D. Artificial intelligence in psychological practice: Current and future applications and implications. Professional Psychology: Research and Practice. 2014; 45(5):332-339. (In Eng.) DOI: https://doi.org/10.1037/a0034559
24. Mohr D., Zhang M., Schueller S.M. Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Annual Review of Clinical Psychology. 2017; 13:23-47. (In Eng.) DOI: https://doi.org/10.1146/annurev-clinpsy-032816-044949
25. Iniesta R., Stahl D., Mcguf P. Machine learning, statistical learning and the future of biological research in psychiatry. Psychological Medicine. 2016; 46(12):2455-2465. (In Eng.) DOI: https://doi.org/10.1017/S0033291716001367
26. Yang L., Jiang D., Han W., Sahli H. DCNN and DNN based multi-modal depression recognition. 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE Press, San Antonio, TX, USA; 2017. p. 484-489. (In Eng.) DOI: https://doi.org/10.1109/ACII.2017.8273643
27. Erickson B.J., Korfiatis P., Akkus Z., Kline T.L. Machine Learning for Medical Imaging. RadioGraphics. 2019; 37(2):505-515. (In Eng.) DOI: https://doi.org/10.1148/rg.2017160130
28. Razzak M.I., Naz S., Zaib A. Deep Learning for Medical Image Processing: Overview, Challenges and the Future. In: Ed. by N. Dey, A. Ashour, S. Borra. Classification in BioApps. Lecture Notes in Computational Vision and Biomechanics. 2018; 26:323-350. Springer, Cham. (In Eng.) DOI: https://doi.org/10.1007/978-3-319-65981-7_12

This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication policy of the journal is based on traditional ethical principles of the Russian scientific periodicals and is built in terms of ethical norms of editors and publishers work stated in Code of Conduct and Best Practice Guidelines for Journal Editors and Code of Conduct for Journal Publishers, developed by the Committee on Publication Ethics (COPE). In the course of publishing editorial board of the journal is led by international rules for copyright protection, statutory regulations of the Russian Federation as well as international standards of publishing.
Authors publishing articles in this journal agree to the following: They retain copyright and grant the journal right of first publication of the work, which is automatically licensed under the Creative Commons Attribution License (CC BY license). Users can use, reuse and build upon the material published in this journal provided that such uses are fully attributed.