Roberta and BERT: Revolutionizing Mental Healthcare through Natural Language
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Abstract
In recent years, developments in natural language processing (NLP) have made it possible for novel applications to be developed in the field of mental healthcare. Roberta and BERT are two examples of these advances. They are examples of state-of-the-art language models that have exhibited extraordinary skills in comprehending and processing human language. Through the use of NLP, this research investigates the revolutionary role that Roberta and BERT have had in the transformation of mental healthcare. via the use of huge quantities of textual data, these models provide insights into the feelings, attitudes, and mental states of patients that are communicated via language that have never been considered before. Through the use of advanced language comprehension methods, Roberta and BERT are able to provide more precise diagnosis, individualized treatment suggestions, and therapeutic interventions that are specifically designed to meet the specific requirements of people. Furthermore, they make it easier to construct virtual assistants and chatbots that are able to provide support and intervention in real time. This allows mental health treatments to be extended to groups who are not currently receiving them and helps to eliminate obstacles that prevent access to these services. However, in order to guarantee the appropriate deployment of these technologies, it is necessary to carefully traverse difficulties such as concerns around privacy, bias in data, and ethical considerations. Despite this, Roberta and BERT have a tremendous amount of promise to revolutionize mental healthcare via the use of natural language understanding. This has the ability to improve results, better relationships between patients and providers, and eventually contribute to the overall well-being of people all over the globe.
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References
Kumar, A., Kumari, J., & Pradhan, J. (2023). Explainable Deep Learning for Mental Health Detection from English and Arabic Social Media Posts. In ACM Transactions on Asian and Low-Resource Language Information Processing. Association for Computing Machinery (ACM).
Ji, S., Zhang, T., Ansari, L., Fu, J., Tiwari, P., & Cambria, E. (2021). MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare. arXiv.
Bokolo, B. G., & Liu, Q. (2023). Deep Learning-Based Depression Detection from Social Media: Comparative Evaluation of ML and Transformer Techniques. In Electronics (Vol. 12, Issue 21, p. 4396). MDPI AG. https://doi.org/10.3390/electronics12214396
Lin, E., Sun, J., Chen, H., & Mahoor, M. H. (2024). Data Quality Matters: Suicide Intention Detection on Social Media Posts Using a RoBERTa-CNN Model (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2402.02262
Rivera, M. J., Teruel, M. A., Maté, A., Trujillo, J.: Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic mapping study. Artificial Intelligence Review 55(2), 1209–1251 (2021).
Abdelhameed, A. M., Bayoumi, M.: Semi-Supervised EEG Signals Classification System for Epileptic Seizure Detection. IEEE Signal Processing Letters 26(12), 1922–1926 (2019).
Acharya, U. R., Sree, S. V., Chattopadhyay, S., Suri, J. S.: Automated Diagnosis Of Normal And Alcoholic EEG Signals. International Journal of Neural Systems 22(03), 1250011 (2012)
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adeli, H.: Deep Convolutional Neural Network for the Automated Detection and Diagnosis of Seizure Using EEG Signals. Computers in Biology and Medicine 100, 270–278 (2018). 0 20 40 60 80 100 120 10 20 30 40 50 60 70 80 90 100 Time Taken in Sec Epoch Conventional Approach Proposed Work 13
Ay, B., Yildirim, O., Talo, M., Baloglu, U. B., Aydin, G., Puthankattil, S. D., Acharya, U. R.: Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals. Journal of Medical Systems 43(7), (2019).
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T.: Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics 19(6), 1236–1246 (2017). Oxford University Press (OUP).
Yang, S., Zhu, F., Ling, X., Liu, Q., Zhao, P.: Intelligent Health Care: Applications of Deep Learning in Computational Medicine. Frontiers in Genetics 12 (2021).
Shamshirband, S., Fathi, M., Dehzangi, A., Chronopoulos, A. T., Alinejad-Rokny, H.: A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues. Journal of Biomedical Informatics 113, 103627 (2021).