Advertisement

Logical-Structural Models of Verbal, Formal and Machine-Interpreted Knowledge Representation in Integrative Scientific Medicine

  • Serhii LupenkoEmail author
  • Oleksandra Orobchuk
  • Mingtang Xu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1080)

Abstract

The paper is devoted to the development of logical-structural models of presentation of knowledge in the field of Integrative Scientific Medicine at verbal, formal and machine-interpreted levels. The logical and structural models of knowledge representation are the basis of the onto-oriented knowledge base of Integrative scientific medicine, reflect the structure of its theory and provide unification, standardization of the technology of presenting information (data and knowledge) as a result of multidisciplinary, interdisciplinary and transdisciplinary studies of national and traditional medical trends that claim for joining the Integrative Scientific Medicine. On the basis of developed models using the OWL language and the Protégé environment, a computer ontology of the special scientific theory of Chinese Image Medicine was created that is used for an onto-oriented expert system, an electronic multimedia education system and a system for professional activities in the field of Chinese Image Medicine as a promising component of Integrative Scientific Medicine.

Keywords

Logical-structural models of knowledge representation Ontology Axiomatic-deductive strategy Onto-oriented informational systems E-learning systems Integrative Medicine Chinese Image Medicine 

References

  1. 1.
    Micozzi, M.S.: Fundamentals of Complementary, Alternative and Integrative Medicine. Copyright: Elsevier, Amsterdam (2019)Google Scholar
  2. 2.
    Fan, D.: Holistic integrative medicine: toward a new era of medical advancement. Front. Med. 11(1), 152 (2017)CrossRefGoogle Scholar
  3. 3.
    Guarneri, E., Horrigan, B., Pechura, C.: The efficacy and cost effectiveness of integrative medicine: a review of the medical and corporate literature. J. Sci. Healing 5, 308–312 (2010)Google Scholar
  4. 4.
    Maizes, V., Rakel, D., Niemiec, C.: Integrative medicine and patient-centered care. J. Sci. Healing 5(5), 277–289 (2009)Google Scholar
  5. 5.
    Horrigan, B.: What is integrative medicine? http://www.bravewell.org/integrative_medicine/what_is_IM. Accessed 23 Nov 2016
  6. 6.
    WHO strategy for traditional medicine for 2014–2023. http://www.who.int/medicines/publications/traditional/trm_strategy14_23/ru/. Accessed 20 Nov 2018
  7. 7.
    Wang, H.: A computerized diagnostic model based on naive Bayesian classifier in traditional Chinese medicine. In: Proceedings of the 1st International Conference on BioMedical Engineering and Informatics (BMEI 2008), pp. 474–477 (2008)Google Scholar
  8. 8.
    Huang, M.-J., Chen, M.-Y.: Integrated design of the intelligent web-based Chinese Medical Diagnostic System (CMDS) – systematic development for digestive health. Expert Syst. Appl. 32(2), 658–673 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Mao, Y., Yin, A.: Ontology modeling and development for Traditional Chinese Medicine. In: Proceedings of the 2nd International Conference on Biomedical Engineering and Informatics (BMEI 2009), pp. 1–5 (2009)Google Scholar
  10. 10.
    Silva, P., et al.: An expert system for supporting Traditional Chinese Medicine diagnosis and treatment. Procedia Technol. 16, 1487–1492 (2014)CrossRefGoogle Scholar
  11. 11.
    Wang, Y., Zhonghua, Y., Jiang, Y., Liu, Y., Chen, L., Liu, Y.: A framework and its empirical study of automatic diagnosis of traditional Chinese medicine utilizing raw free-text clinical records. J. Biomed. Inform. 45(2), 210–223 (2012)CrossRefGoogle Scholar
  12. 12.
    Wang, X., Qu, H., Liu, P., Cheng, Y.: A self-learning expert system for diagnosis in traditional Chinese medicine. Expert Syst. Appl. 26(4), 557–566 (2004)CrossRefGoogle Scholar
  13. 13.
    Lukman, S., He, Y., Hui, S.C.: Computational methods for traditional Chinese medicine: a survey. Comput. Methods Programs Biomed. 88, 283–294 (2007)CrossRefGoogle Scholar
  14. 14.
    Chen, H., Wang, Y., Wang, H., et al.: Towards a semantic web of relational databases: a practical semantic toolkit and an in-use case from traditional Chinese medicine. In: Proceeding of the 5th International Conference on The Semantic Web (ISWC 2006), pp. 750–763 (2006)Google Scholar
  15. 15.
    Atemezing, G., Pavón, J.: An ontology for African traditional medicine. In: Corchado, J.M., Rodríguez, S., Llinas, J., Molina, J.M. (eds.) International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008). Advances in Soft Computing, vol. 50. Springer, Heidelberg (2009)Google Scholar
  16. 16.
    Gayathria, M., Jagadeesh Kannan, R.: Ontology based Indian medical system. Mater. Today Proc. 5(1), 1974–1979 (2018)CrossRefGoogle Scholar
  17. 17.
    Zhu, X., et al.: Research on classification of Tibetan medical syndrome in chronic atrophic gastritis. Appl. Sci. 9, 1664 (2019)CrossRefGoogle Scholar
  18. 18.
    International program of scientific research in Chinese Image Medicine and Zhong Yuan Qigong for 2017–2023. https://kundawell.com/ru/mezhdunarodnaya-programma-nauchnykh-issledovanij-kitajskoj-imidzh-meditsiny-i-chzhun-yuan-tsigun-na-2017-2023-god. Accessed 15 Nov 2018
  19. 19.
    Lupenko, S., Orobchuk, O., Vakulenko, D., Sverstyuk, A., Horkunenko, A.: Integrated onto-based information analytical environment of scientific research, professional healing and e-learning of Chinese Image Medicine. Inf. Sys. Net. J., 10–19 (2017)Google Scholar
  20. 20.
    Lupenko, S., Pavlyshyn, A., Orobchuk, O.: Conceptual fundamentals for ontological simulation of Chinese Image Medicine as a promising component of integrative medcine. Sci. Edu. New. Dim. J. 15, 28–32 (2017)Google Scholar
  21. 21.
    Lupenko, S., Pasichnyk, V., Kunanets, N., Orobchuk, O., Xu, M.: The axiomatic-deductive strategy of knowledge organization in onto-based e-learning systems for Chinese Image Medicine. In: Proceedings of the 1st International Workshop on Informatics & Data-Driven Medicine, vol. 2255, pp. 126–134 (2018)Google Scholar
  22. 22.
    Lupenko, S., Orobchuk, O., Zahorodna, N.: Formation of the onto-oriented electronic educational environment as a direction the formation of integrated medicine using the example of CIM. In: Actual Scientific Research in the Modern World: Collection of Scientific Papers of the XXIII International Scientific Conference, vol. 12(32), pp. 56–61 (2017)Google Scholar
  23. 23.
    Lupenko, S., Orobchuk, O., Pomazkina, T., Xu, M.: Conceptual, formal and software-information fundamentals of ontological modeling of Chinese Image Medicine as an element of integrative medicine. Wor. Sci. 1 (2017)Google Scholar
  24. 24.
    Lupenko, S., Orobchuk, O., Xu, M.: The ontology as the core of integrated information environment of Chinese Image Medicine. In: Advances in Computer Science for Engineering and Education II, ICCSEEA 2019. Advances in Intelligent Systems and Computing, vol. 938, pp. 471–481. Springer, Cham (2020)Google Scholar
  25. 25.
    Lupenko, S.: Axiomatic-deductive strategy of the organization of the content of academic discipline in the field of information technologies using the ontological approach. In: IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), Lviv, vol. 1, pp. 387–390 (2018)Google Scholar
  26. 26.
    Lupenko, S.: Organization of the content of academic discipline in the field of information technologies using ontological approach. In: Proceeding of the International Conference on CSIT. Advances in Intelligent Systems and Computing III, Lviv, pp. 312–327 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Ternopil Ivan Puluj National Technical UniversityTernopilUkraine
  2. 2.Beijing Medical Research Institute “Kundawell”BeijingChina

Personalised recommendations