Advertisement

Intelligent Diagnosis and Treatment Research of Knee Osteoarthritis Based on Big Data

  • Xin LiEmail author
  • Guigang Zhang
  • Chunxiao Xing
  • Yong Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10983)

Abstract

Knee Osteoarthritis (KOA) is a common and frequently-occurring chronic disease. The traditional KOA diagnosis lacks personalized and systematic diagnosis and treatment models, and lacks high-quality and large-sample randomized controlled clinical studies. In this paper, we propose a kind of intelligent diagnosis and treatment method for KOA based on big data and artificial intelligence.

Keywords

Knee osteoarthritis (KOA) Big data Artificial intelligence 

Notes

Acknowledgment

This work was supported by NSFC (91646202), Research/Project 2017YB142 supported by Ministry of Education of The People’s Republic of China, the 1000-Talent program.

References

  1. 1.
    Bhargavi, R., Pathak, R., Vaidehi, V.: Dynamic complex event processing — adaptive rule engine. In: International Conference on Recent Trends in Information Technology (ICRTIT), pp. 189–194 (2013)Google Scholar
  2. 2.
    Forgy, C.L.: RETE: a fast algorithm for the many pattern/many object pattern match problem. Artif. Intell. 19, 17–37 (1982)CrossRefGoogle Scholar
  3. 3.
    Miranker, D.P.: TREAT: a better match algorithm for AI production systems. In: proceedings of AAAI 87 Conference on Artificial Intelligence, pp. 42–47 August 1987Google Scholar
  4. 4.
    Long, M.J., Papi, E., Duffell, L.D., McGregor, A.H.: Predicting knee osteoarthritis risk in injured populations. Clin. Biomech. (Bristol, Avon) 47, 87–95 (2017)CrossRefGoogle Scholar
  5. 5.
    Uritani, D., Fukumoto, T., Myodo, T., Fujikawa, K., Usui, M., Tatara, D.: The association between toe grip strength and osteoarthritis of the knee in Japanese women: a multicenter cross-sectional study. PLoS ONE 12(10), e0186454 (2017)CrossRefGoogle Scholar
  6. 6.
    Hoffart, J., Suchanek, F.M., Berberich, K., et al.: YAGO2: exploring and querying world knowledge in time, space, context, and many languages. In: International Conference on World Wide Web, WWW, Hyderabad, India, 28 March–April, pp. 229–232 (2011)Google Scholar
  7. 7.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-76298-0_52CrossRefGoogle Scholar
  8. 8.
    Carlson, A., Betteridge, J., Wang, R.C., et al.: Coupled semi-supervised learning for information extraction. In: ACM International Conference on Web Search & Data Mining, pp. 101–110. ACM (2010)Google Scholar
  9. 9.
    Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(Database issue), D267–D270 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xin Li
    • 1
    Email author
  • Guigang Zhang
    • 2
  • Chunxiao Xing
    • 3
    • 4
    • 5
    • 6
  • Yong Zhang
    • 3
    • 4
    • 5
    • 6
  1. 1.Department of RehabilitationBeijing Tsinghua Changgung HospitalBeijingChina
  2. 2.Institute of AutomationChinese Academy of SciencesBeijingChina
  3. 3.Research Institute of Information TechnologyBeijingChina
  4. 4.Beijing National Research Center for Information Science and TechnologyBeijingChina
  5. 5.Department of Computer Science and TechnologyBeijingChina
  6. 6.Institute of Internet IndustryTsinghua UniversityBeijingChina

Personalised recommendations