Research Article
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Year 2023, Volume: 3 Issue: 2, 113 - 122, 01.10.2023

Abstract

References

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  • [8] J. Luis, Z. Montenegro, C. André Da Costa, and L. P. Janssen, “Evaluating the use of chatbot during pregnancy: A usability study,” Healthcare Analytics, vol. 2, p. 100072, 2022, doi: 10.1016/j.health.2022.100072.
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  • [10] S. Pandey and S. Sharma, “A comparative study of retrieval-based and generative-based chatbots using Deep Learning and Machine Learning,” Healthcare Analytics, vol. 3, p. 100198, 2023, doi: 10.1016/j.health.2023.100198.
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  • [13] Y.-L. Liu, B. Hu, W. Yan, and Z. Lin, “Can chatbots satisfy me? A mixed-method comparative study of satisfaction with task-oriented chatbots in mainland China and Hong Kong,” 2023, doi: 10.1016/j.chb.2023.107716.
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  • [16] H. Wang, C. Focke, R. Sylvester, N. Mishra, and W. Wang, “Fine-tune Bert for DocRED with Two-step Process”, Accessed: Jun. 06, 2023. [Online]. Available: https://github.com/
  • [17] X. Deng, Q. Liu, Y. Deng, and S. Mahadevan, “An improved method to construct basic probability assignment based on the confusion matrix for classification problem,” Inf Sci (N Y), pp. 250–261, 2016, doi: 10.1016/j.ins.2016.01.033.
  • [18] “Classification Model Evaluation Metrics”, doi: 10.14569/IJACSA.2021.0120670.

Artificial Intelligence Based Chatbot in E-Health System

Year 2023, Volume: 3 Issue: 2, 113 - 122, 01.10.2023

Abstract

The healthcare sector is undergoing a digital revolution due to the rapid growth of technology, and AI technologies are becoming more commonplace in the sector. Chatbots have become useful resources for people to get advice and information about their health issues. The creation and implementation of an AI-based chatbot, integrated with an e-health system, is the main topic of this article. This paper explains the development and creation of chatbots. The chatbot's language comprehension and response capabilities are enhanced through the use of AI techniques such as machine learning and natural language processing (NLP). In addition, the chatbot's user interaction procedure and data security precautions are covered. The paper also examines how the developed chatbot can be integrated into an e-health platform and provides the results of user testing. These evaluations focus on the chatbot's ability to provide accurate and insightful answers, understand user requirements, and provide useful advice. The test results show favourable user evaluations and indicate how well the AI-based chatbot performs in providing healthcare services.

References

  • [1] C. Zielinski et al., “Chatbots, ChatGPT, and Scholarly Manuscripts - WAME Recommendations on ChatGPT and Chatbots in Relation to Scholarly Publications,” Afro-Egyptian Journal of Infectious and Endemic Diseases, vol. 13, no. 1, pp. 75–79, Mar. 2023, doi: 10.21608/AEJI.2023.282936.
  • [2] N. Bhirud, S. Tatale, S. Randive, S. Tataale, and S. Nahar, “A Literature Review On Chatbots In Healthcare Domain Computational Feasibility of Paninian Grammar for Indian Languages’ Analyses View project Machine Learning View project A Literature Review On Chatbots In Healthcare Domain,” International Journal Of Scientific & Technology Research, vol. 8, p. 7, 2019, Accessed: Jun. 06, 2023. [Online]. Available: www.ijstr.org
  • [3] S. Laumer, C. Maier, F. Tobias Gubler, and F. Tobias, “Chatbot Acceptance In Healthcare: Explaining User Adoption Of Conversational Agents For Disease Diagnosis,” 2019, Accessed: Jun. 06, 2023. [Online]. Available: https://aisel.aisnet.org/ecis2019_rp/88
  • [4] “View of Doctor Recommendation Chatbot: A research study.” https://sabapub.com/index.php/jaai/article/view/310/240 (accessed Jun. 06, 2023).
  • [5] Y. Windiatmoko, R. Rahmadi, A. F. Hidayatullah, R. Pradhan, J. Shukla, and M. Bansal, “K-Bot’ Knowledge Enabled Personalized Healthcare Chatbot”, doi: 10.1088/1757-899X/1116/1/012185.
  • [6] M.-Y. Huang, C.-S. Weng, H.-L. Kuo, and Y.-C. Su, “Using a chatbot to reduce emergency department visits and unscheduled hospitalizations among patients with gynecologic malignancies during chemotherapy: A retrospective cohort study,” 2023, doi: 10.1016/j.heliyon.2023.e15798.
  • [7] J. P. Rainey et al., “A Multilingual Chatbot Can Effectively Engage Arthroplasty Patients With Limited English Proficiency,” 2023, doi: 10.1016/j.arth.2023.04.014.
  • [8] J. Luis, Z. Montenegro, C. André Da Costa, and L. P. Janssen, “Evaluating the use of chatbot during pregnancy: A usability study,” Healthcare Analytics, vol. 2, p. 100072, 2022, doi: 10.1016/j.health.2022.100072.
  • [9] E. D. Liddy, “Natural Language Processing Natural Language Processing Natural Language Processing 1,” 2001, Accessed: Jun. 06, 2023. [Online]. Available: https://surface.syr.edu/istpub
  • [10] S. Pandey and S. Sharma, “A comparative study of retrieval-based and generative-based chatbots using Deep Learning and Machine Learning,” Healthcare Analytics, vol. 3, p. 100198, 2023, doi: 10.1016/j.health.2023.100198.
  • [11] A. Graves, N. Jaitly, and A.-R. Mohamed, “Hybrıd Speech Recognition With Deep Bidirectional LSTM”.
  • [12] J. Kapoči and ¯ Ut˙ E-Dzikien˙, “A Domain-Specific Generative Chatbot Trained from Little Data”, doi: 10.3390/app10072221.
  • [13] Y.-L. Liu, B. Hu, W. Yan, and Z. Lin, “Can chatbots satisfy me? A mixed-method comparative study of satisfaction with task-oriented chatbots in mainland China and Hong Kong,” 2023, doi: 10.1016/j.chb.2023.107716.
  • [14] “zl111/ChatDoctor · Hugging Face.” https://huggingface.co/zl111/ChatDoctor (accessed Jun. 06, 2023).
  • [15] J. Devlin, M.-W. Chang, K. Lee, K. T. Google, and A. I. Language, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”, Accessed: Jun. 06, 2023. [Online]. Available: https://github.com/tensorflow/tensor2tensor
  • [16] H. Wang, C. Focke, R. Sylvester, N. Mishra, and W. Wang, “Fine-tune Bert for DocRED with Two-step Process”, Accessed: Jun. 06, 2023. [Online]. Available: https://github.com/
  • [17] X. Deng, Q. Liu, Y. Deng, and S. Mahadevan, “An improved method to construct basic probability assignment based on the confusion matrix for classification problem,” Inf Sci (N Y), pp. 250–261, 2016, doi: 10.1016/j.ins.2016.01.033.
  • [18] “Classification Model Evaluation Metrics”, doi: 10.14569/IJACSA.2021.0120670.
There are 18 citations in total.

Details

Primary Language English
Subjects Natural Language Processing
Journal Section Research Articles
Authors

Kamil Akarsu 0000-0001-7715-2801

Orhan Er 0000-0002-4732-9490

Publication Date October 1, 2023
Published in Issue Year 2023 Volume: 3 Issue: 2

Cite

APA Akarsu, K., & Er, O. (2023). Artificial Intelligence Based Chatbot in E-Health System. Artificial Intelligence Theory and Applications, 3(2), 113-122.