Pregcare Record Analysis Based on BERT and  Anticipatory Computing: A Review

Volume: 10 | Issue: 01 | Year 2024 | Subscription
International Journal of Analog Integrated Circuits
Received Date: 05/06/2024
Acceptance Date: 06/24/2024
Published On: 2024-06-30
First Page: 27
Last Page: 35

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By: Selva Kumar, Omar Abdulla Sherief, Vignesh V, Sreenath M, and Dhanashri Shanbhag

1- Assistant Professor, Department of Computer Science and Engineering, Bhusanayana Mukundadas Sreenivasaiah (BMS) College of Engineering, India
2- Student, Department of Computer Science and Engineering, Bhusanayana Mukundadas Sreenivasaiah (BMS) College of Engineering, India
3- Student, Department of Computer Science and Engineering, Bhusanayana Mukundadas Sreenivasaiah (BMS) College of Engineering, India
4- Student, Department of Computer Science and Engineering, Bhusanayana Mukundadas Sreenivasaiah (BMS) College of Engineering, India
5- Student, Department of Computer Science and Engineering, Bhusanayana Mukundadas Sreenivasaiah (BMS) College of Engineering, India

Abstract

Development of a sophisticated Medical Pregnancy Care chatbot leveraging state- of-the-art Natural Language Processing (NLP) techniques, specifically the BERT framework. The chatbot offers a seamless user experience with two key functionalities: patient registration and comprehensive medical report generation. New users are prompted to register, providing essential personal details, and subsequently prompted to fill detailed medical reports, ranging from the 1st to the 9th month of pregnancy. The system employs anticipatory computing strategies for dynamic document storage during runtime, ensuring efficient and secure handling of sensitive patient data. The uniqueness of the proposed idea lies in its utilization of the NLP BERT framework to analyse the content of each medical report comprehensively. The system employs anticipatory computing, intelligently predicting and organizing document storage implemented the evolving nature of pregnancy reports. As the user progresses through each month, the chatbot dynamically generates summarized PDF documents for each medical report, incorporating insights derived from NLP analysis. This methodology not only helps in efficient document management but also provides healthcare professionals with concise, actionable information. The domain basically is focused on combining the concept of Anticipatory computing and BERT Framework. The ‘EXISTING SYSTEM’ already uses technologies using LLM but it is not implemented for document summarization and just involves direct extraction of data from the database unlike the existing system.

Keywords: Anticipatory Computing, BERT, QR Code, Document Summarization ,LLM, NLP, Interface, CNN

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Citation:

How to cite this article: Selva Kumar, Omar Abdulla Sherief, Vignesh V, Sreenath M, and Dhanashri Shanbhag, Pregcare Record Analysis Based on BERT and  Anticipatory Computing: A Review. International Journal of Analog Integrated Circuits. 2024; 10(01): 27-35p.

How to cite this URL: Selva Kumar, Omar Abdulla Sherief, Vignesh V, Sreenath M, and Dhanashri Shanbhag, Pregcare Record Analysis Based on BERT and  Anticipatory Computing: A Review. International Journal of Analog Integrated Circuits. 2024; 10(01): 27-35p. Available from:https://journalspub.com/publication/pregcare-record-analysis-based-on-bert-and-anticipatory-computing-a-review/
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Refrences:

  1. Lee, Hyeonhoon, Jaehyun Kang, and Jonghyeon Yeo. “Medical specialty recommendations by an artificial intelligence chatbot on a smartphone: development and ” Journal of medical Internet research 23, no. 5 (2021): e27460.
  2. Kim, Jin K., Michael Chua, Mandy Rickard, and Armando Lorenzo. “ChatGPT and large language model (LLM) chatbots: the current state of acceptability and a proposal for guidelines on utilization in academic ” Journal of Pediatric Urology (2023).
  3. Kim, Jin K., Michael Chua, Mandy Rickard, and Armando Lorenzo. “ChatGPT and large language model (LLM) chatbots: the current state of acceptability and a proposal for guidelines on utilization in academic ” Journal of Pediatric Urology (2023).
  4. Kim, Jin K., Michael Chua, Mandy Rickard, and Armando Lorenzo. “ChatGPT and large language model (LLM) chatbots: the current state of acceptability and a proposal for guidelines on utilization in academic ” Journal of Pediatric Urology (2023).
  5. Kim, Jin K., Michael Chua, Mandy Rickard, and Armando Lorenzo. “ChatGPT and large language model (LLM) chatbots: the current state of acceptability and a proposal for guidelines on utilization in academic ” Journal of Pediatric Urology (2023).
  6. Kim, Jin K., Michael Chua, Mandy Rickard, and Armando Lorenzo. “ChatGPT and large language model (LLM) chatbots: the current state of acceptability and a proposal for guidelines on utilization in academic ” Journal of Pediatric Urology (2023).
  7. Kim, Jin K., Michael Chua, Mandy Rickard, and Armando Lorenzo. “ChatGPT and large language model (LLM) chatbots: the current state of acceptability and a proposal for guidelines on utilization in academic ” Journal of Pediatric Urology (2023).
  8. Shin, Hyeong Jin, Jeong Yeon Park, Dae Bum Yuk, and Jae Sung Lee. “BERT-based spatial information “
  9. In Proceedings of the Third International Workshop on Spatial Language Understanding, pp. 10-17. 2020
  10. Noor, Ahmed , W. Scott Burton, and Charles
  11. Bert. “Computational models for sandwich panels and shells.” (1996): 155-199.
  12. Hao, Yaru, Li Dong, Furu Wei, and Ke Xu. “Visualizing and understanding the effectiveness of ” arXiv preprint arXiv:1908.05620 (2019).
  13. Pejovic,Veljko, and Mirco “Anticipatory mobile computing for behaviour change interventions.” In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, pp. 1025-1034. 2014.
  14. Pejovic,Veljko, and Mirco “Anticipatory mobile computing for behaviour change interventions.” In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, pp. 1025-1034. 2014.
  15. Nadin, M., 2010. Anticipatory computing: from a high-level theory to hybrid computing
  16. Nadin, “Predictive and anticipatory computing.” Encyclopedia of computer science and technology, 2nd edn. Taylor and Francis, London (2017): 643-659.
  17. Nadin, , 2000.  Anticipatory computing. Ubiquity, 2000(December), pp.2-es.
  18. Lin, Chunpei, Guanxi Zhao, Yenchun Jim Wu, and Hailin Li. “Anticipatory computing for human behavioral change intervention: A systematic review.” IEEE Access 7 (2019): 103738-
  19. Pejovic, V. and Musolesi, M., 2014, September. Anticipatory mobile computing for behaviour change interventions. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication (pp. 1025-1034).
  20. Nadin, , 2000.  Anticipatory computing. Ubiquity, 2000(December), pp.2-es.
  21. Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. “Bert: Pre-training of deep bidirectional transformers for language ” arXivpreprint
  22. arXiv:1810.04805 (2018).
  23. Kenton, J.D.M.W.C. and Toutanova, L.K., 2019, June. Bert: Pre-training of deep bidirectional transformers for language In Proceedings of naacL-HLT (Vol. 1, p. 2).
  24. Jawahar, Ganesh, Benoît Sagot, and Djamé “What does BERT learn about the structure of language?.” In ACL 2019-57th Annual Meeting of the Association for Computational Linguistics. 2019.

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