A Scoping Review on the Use of Artificial Neural Networks (ANNs) in Decision Adoption Systems (DAS) for Psychological Data Interpretation
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Abstract
In the past decade, there has been a surge of interest in the potential of artificial neural networks (ANNs) to enhance decision-making processes in a variety of domains, including decision adoption systems (DAS). This scoping review focuses on the utilization of ANNs in DAS for the interpretation of psychological data. The major goal of this scoping review is to determine the current status of research on the usage of ANNs in DAS for psychological data interpretation and highlight the advantages and limitations of using ANNs in this context. Through a comprehensive search of relevant databases, a range of studies were identified that have employed ANNs in DAS for psychological data interpretation. These studies demonstrate the potential of ANNs to improve decision-making processes in psychological data interpretation, resulting in more accurate diagnoses.. The scoping review presents information on the actual status of research on the usage of ANNs in DAS for psychological data interpretation, offering a comprehensive understanding of the potential of ANNs in enhancing decision-making processes in this context. As such, the findings of this review have significant implications for future research on ANNs in DAS for psychological data interpretation, which may result in the development of more effective decision-making tools for this purpose.
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References
Ahmadi, M., & Nopour, R. (2022). Clinical decision support system for quality of life among the elderly: an approach using artificial neural network. BMC Medical Informatics and Decision Making, 22(1), 293.
Averkin, A., Bazarkina, D., Pantserev, K., & Pashentsev, E. (2019, August). Artificial intelligence in the context of psychological security: Theoretical and practical implications. In 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019) (pp. 101-107). Atlantis Press.
Bertl, M., Metsallik, J., & Ross, P. (2022). A systematic literature review of AI-based digital decision support systems for post-traumatic stress disorder. Frontiers in Psychiatry, 13.
Bird, J. J., Ekart, A., Buckingham, C. D., & Faria, D. R. (2019, April). Mental emotional sentiment classification with an eeg-based brain-machine interface. In Proceedings of theInternational Conference on Digital Image and Signal Processing (DISP’19).
Bouaiachi, Y., Khaldi, M., & Azmani, A. (2014, October). Neural network-based decision support system for pre-diagnosis of psychiatric disorders. In 2014 Third IEEE International Colloquium in Information Science and Technology (CIST) (pp. 102-106). IEEE.
Buckingham, C. (2002). Psychological cue use and implications for a clinical decision support system. Medical Informatics and the Internet in Medicine, 27(4), 237-251.
Dabek, F., & Caban, J. J. (2015). A neural network based model for predicting psychological conditions. In Brain Informatics and Health: 8th International Conference, BIH 2015, London, UK, August 30-September 2, 2015. Proceedings 8 (pp. 252-261). Springer International Publishing.
Delavarian, M., Towhidkhah, F., Dibajnia, P., & Gharibzadeh, S. (2012). Designing a decision support system for distinguishing ADHD from similar children behavioral disorders. Journal of medical systems, 36, 1335-1343.
Ding, J., Wang, J., Wu, P., Huang, Y., Dong, Y., Rong, N., & Wang, X. (2022). Identification of psychological stressors in cancer patients based on a computer decision support nursing system. American Journal of Translational Research, 14(10), 6953.
Ekong, V. E., Inyang, U. G., & Onibere, E. A. (2012). Intelligent decision support system for depression diagnosis based on neuro-fuzzy-CBR hybrid. Modern Applied Science, 6(7), 79.
Fathi, S., Ahmadi, M., Birashk, B., & Dehnad, A. (2020). Development and use of a clinical decision support system for the diagnosis of social anxiety disorder. Computer Methods and Programs in Biomedicine, 190, 105354.
Götz, F. M., Maertens, R., Loomba, S., & van der Linden, S. (2023). Let the algorithm speak: How to use neural networks for automatic item generation in psychological scale development. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000540
Higgins, J. P., Altman, D. G., Gøtzsche, P. C., Jüni, P., Moher, D., Oxman, A. D., ... & Sterne, J. A. (2011). The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. Bmj, 343.
Kanimozhiselvi, C. S., & Pratap, A. (2016). Possibilistic LVQ neural network-An application to childhood autism grading. Neural Network World, 26(3), 253.
Livieris, I., Mikropoulos, T., & Pintelas, P. (2016). A decision support system for predicting students’ performance. Themes in Science and Technology Education, 9(1), 43-57.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group*, T. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Annals of internal medicine, 151(4), 264-269.
Moridis, C. N., & Economides, A. A. (2009). Prediction of student’s mood during an online test using formula-based and neural network-based method. Computers & Education, 53(3), 644-652.
Rad, D., Magulod Jr, G. C., Balas, E., Roman, A., Egerau, A., Maier, R., ... & Chis, R. (2022). A Radial Basis Function Neural Network Approach to Predict Preschool Teachers' Technology Acceptance Behavior. Frontiers in psychology, 13, 880753.
Razzouk, D., Mari, J. D. J., Shirakawa, I., Wainer, J., & Sigulem, D. (2006). Decision support system for the diagnosis of schizophrenia disorders. Brazilian journal of medical and biological research, 39, 119-128.
Sharma, S., Singh, G., & Sharma, M. (2021). A comprehensive review and analysis of supervised-learning and soft computing techniques for stress diagnosis in humans. Computers in Biology and Medicine, 134, 104450.
Suhasini, A., Palanivel, S., & Ramalingam, V. (2011). Multimodel decision support system for psychiatry problem. Expert Systems with Applications, 38(5), 4990-4997.
Tian, X., & Pu, Y. (2008). An artificial neural network approach to hotel employee satisfaction: The case of China. Social Behavior and Personality: an international journal, 36(4), 467-482.
Whiting, P. F., Rutjes, A. W., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., ... & QUADAS-2 Group*. (2011). QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Annals of internal medicine, 155(8), 529-536.
Yessefi, A. E., Ezziyyani, M., Cherrat, L., & Ech-Charrat, M. R. (2020). Smarter Crossing Analytics System to Predict and Anticipate the Student Behavior for Self Automatic Adaptation of Academic Learning. In Advanced Intelligent Systems for Sustainable Development (AI2SD’2019) Volume 1-Advanced Intelligent Systems for Education and Intelligent Learning System (pp. 199-209). Springer International Publishing.