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Question Answering System-Based Chatbot for Health care

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Proceedings of the Global AI Congress 2019

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1112))

Abstract

Chatbot helps to provide automated as well as instant output at the absence of human intervention. It is more essential in an emerging domain like health care to manage the emergency condition without the presence of medical experts. In this research, we are motivated to develop a health-care chatbot system to recognize diseases from user-provided health conditions or symptoms. This research helps to overcome the above-mentioned challenges in partially. Primarily, these challenges are introduced due to the rapid development of information and communication technology. On the other hand, the chatbot industry is rapidly growing while promising to cut the costs. Also, less involvement of domain experts and lack of automated information extraction system introduced more difficulties in this task. Hence, we have employed an unsupervised machine learning technique to build this chatbot. Additionally, we have prepared an experimental dataset that assists in validating the output of the proposed system. Primarily, this system recognized a set of diseases from the user given a set of symptoms and vice versa.

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Notes

  1. 1.

    https://chatbotsmagazine.com/how-chatbots-will-shape-the-future-of-healthcare-fa8e30cebb1c.

  2. 2.

    https://www.medicinenet.com/.

References

  1. Turing, A.M.: Computing machinery and intelligence. In: Epstein, R., Roberts, G., Beber, G. (eds.) Parsing the Turing Test. Springer, Dordrecht (2009)

    Google Scholar 

  2. Weizenbaum, J.: ELIZA—a computer program for the study of natural language communication between man and machine. Commun. ACM 9(1), 36–45 (1966)

    Article  Google Scholar 

  3. Mauldin, M.L.: Chatterbots, tinymuds, and the turing test: entering the loebner prize competition. In: AAAI, vol. 94, pp. 16–21 (1994)

    Google Scholar 

  4. Ni, L., Lu, C., Liu, N., Liu, J.: Mandy: towards a smart primary care chatbot application. In: International Symposium on Knowledge and Systems Sciences, pp. 38–52. Springer, Singapore (2017)

    Google Scholar 

  5. Brindha, G.: Emerging trends of telemedicine in India. Indian J. Sci. Technol. 6(sup 5) (2013)

    Google Scholar 

  6. Meskó, Bertalan, Hetényi, Gergely, Győrffy, Zsuzsanna: Will artificial intelligence solve the human resource crisis in healthcare? BMC Health Serv. Res. 18(1), 545 (2018)

    Article  Google Scholar 

  7. Al–Juboury, A.W., AL-Assadi, M.K., Ali, A.M.: Seroprevalence of Hepatitis B and C among blood donors in Babylon Governorate-Iraq. Med. J. Babylon 7(1–2), 121–129 (2010)

    Google Scholar 

  8. DeVault, D., Artstein, R., Benn, G., Dey, T., Fast, E., Gainer, A., Georgila, K., Gratch, J., Hartholt, A., Lhommet, M., Lucas, G.: SimSensei Kiosk: a virtual human interviewer for healthcare decision support. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, pp. 1061–1068. International Foundation for Autonomous Agents and Multiagent Systems (2014)

    Google Scholar 

  9. Okokpujie, K.O., Orimogunje, A., Noma-Osaghae, E., Alashiri, O.: An Intelligent Online Diagnostic System With Epidemic Alert. Int. J. Innov. Sci. Res. Technol. 2(9) (2017)

    Google Scholar 

  10. Dharwadkar, R., Deshpande, N.A.: A Medical ChatBot. Int. J. Comput. Trends Technol. (IJCTT) 60(1), 41–45 (2018). ISSN: 2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group

  11. Hsu, H.-H., Huang, N.-F.: Xiao-Shih: the educational intelligent question answering bot on Chinese-based MOOCs. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1316–1321 (2018)

    Google Scholar 

  12. Wen, M.-H.: A conversational user interface for supporting individual and group decision-making in stock investment activities. In: 2018 IEEE International Conference on Applied System Invention (ICASI), pp. 216–219 (2018)

    Google Scholar 

  13. Amato, F., Marrone, S., Moscato, V., Piantadosi, G., Picariello, A., Sansone, C.: Chatbots Meet eHealth: automatizing healthcare. In: WAIAH@ AI* IA, pp. 40–49 (2017)

    Google Scholar 

  14. Comendador, B.E.V., Francisco, B.M.B., Medenilla, J.S., Nacion, S.M.T., Serac, T.B.E.: Pharmabot: a pediatric generic medicine consultant chatbot. J. Autom. Control. Eng. 3(2), 137–140 (2015). https://doi.org/10.12720/joace.3.2.137-140

  15. Merges, F., Holland, A., Schneider, S., Fathi, M.: Knowledge-based medical system integration to foster knowledge transfer and network building. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics, pp. 2951–2957. IEEE (2011)

    Google Scholar 

  16. Mondal, A., Das, D., Cambria, E., Bandyopadhyay, S.: Wme 3.0: an enhanced and validated lexicon of medical concepts. In: Proceedings of the Ninth Global WordNet Conference (2018)

    Google Scholar 

  17. Mondal, A., Das, D., Cambria, E., Bandyopadhyay, S.: Wme: sense, polarity and affinity based concept resource for medical events. In: Proceedings of the Eighth Global WordNet Conference, pp. 242–246 (2016)

    Google Scholar 

  18. Mondal, A., Cambria, E., Feraco, A., Das, D., Bandyopadhyay, S.: Auto-categorization of medical concepts and contexts. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–7. IEEE (2017)

    Google Scholar 

  19. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)

    Google Scholar 

  20. Hartigan, J.A., Wong, M.A.: Algorithm AS 136: a k-means clustering algorithm. J. R. Stat. Society. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)

    Google Scholar 

  21. Sculley, D.: Web-scale k-means clustering. In: Proceedings of the 19th International Conference on World Wide Web, pp. 1177–1178. ACM (2010)

    Google Scholar 

  22. Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)

    Article  Google Scholar 

  23. Feizollah, A., Anuar, N.B., Salleh, R., Amalina, F.: Comparative study of k-means and mini batch k-means clustering algorithms in android malware detection using network traffic analysis. In: 2014 International Symposium on Biometrics and Security Technologies (ISBAST), pp. 193–197. IEEE (2014)

    Google Scholar 

  24. Mondal, A., Cambria, E., Das, D., Hussain, A., Bandyopadhyay, S.: Relation extraction of medical concepts using categorization and sentiment analysis. Cogn. Comput. 1–16 (2018)

    Google Scholar 

  25. Mondal, A., Das, D., Bandyopadhyay, S.: Relationship extraction based on category of medical concepts from lexical contexts. In: Proceedings of 14th International Conference on Natural Language Processing (ICON), pp. 212–219 (2017)

    Google Scholar 

  26. Mondal, A., Chaturvedi, I., Das, D., Bajpai, R., Bandyopadhyay, S.: Lexical resource for medical events: a polarity based approach. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp. 1302–1309. IEEE (2015)

    Google Scholar 

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Correspondence to Sharob Sinha .

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Sinha, S., Mandal, S., Mondal, A. (2020). Question Answering System-Based Chatbot for Health care. In: Mandal, J., Mukhopadhyay, S. (eds) Proceedings of the Global AI Congress 2019. Advances in Intelligent Systems and Computing, vol 1112. Springer, Singapore. https://doi.org/10.1007/978-981-15-2188-1_6

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