Advertisement

Multi-level medical periodic patterns from human movement behaviors

  • Dongzhi Zhang
  • Kyungmi Lee
  • Ickjai LeeEmail author
Article
Part of the following topical collections:
  1. Special Issue on Application of Artificial Intelligence in Health Research

Abstract

Human movement behaviors could reveal many interesting medical patterns. Due to the advances in location-aware devices, a large volume of human movement behaviors has been captured in the form of spatio-temporal trajectories. These spatio-temporal trajectories are useful resources for medical data mining, and they could be used to classify which trajectory passes through medical centres and which one does not. Traditional approaches utilise time-series datasets while ignoring spatio-temporal semantics in order to detect periodic patterns in medical domains. They also fail to consider the inherent hierarchical nature of patterns. We investigate a medical data mining framework that generates multi-level medical periodic patterns. A Geolife dataset is used to test the feasibility and applicability of our framework. Experiments demonstrate that the proposed framework successfully distinguishes those who periodically visit medical centres from those who do not, and also to find multi-level medical periodic patterns revealing interesting hierarchical medical behaviours. One potential application includes an automated personalised medical service. For instance, medical institutions can send personalised relative medicine information to people who regularly visit certain medical centres. It will be useful for the discovery and diagnosis of diseases for patients.

Keywords

Periodic pattern mining Multi-level hierarchical patterns Spatio-temporal trajectories Medical patterns 

References

  1. 1.
    Bar-David S, Bar-David I, Cross P, Ryan SJ, Knechtel CU, Getz WM. Methods for assessing movement path recursion with application to African buffalo in South Africa. Ecology. 2009;90(9):2467–79.CrossRefGoogle Scholar
  2. 2.
    Berlingerio M, Bonchi F, Giannotti F, Turini F. Mining clinical data with a temporal dimension: a case study. In: IEEE 2007 IEEE International conference on bioinformatics and biomedicine; 2007. p. 429–36.Google Scholar
  3. 3.
    Boytcheva S, Angelova G, Angelov Z, Tcharaktchiev D. Mining comorbidity patterns using retrospective analysis of big collection of outpatient records. Health Inf Sci Syst. 2017;5(1):3.CrossRefGoogle Scholar
  4. 4.
    Cao H, Cheung DW, Mamoulis N. Discovering partial periodic patterns in discrete data sequences. In: 8th Pacific-Asia conference on advances in knowledge discovery and data mining (PAKDD), Berlin: Springer; 2004. p. 653–58.CrossRefGoogle Scholar
  5. 5.
    Cao H, Mamoulis N, Cheung DW. Discovery of periodic patterns in spatiotemporal sequences. IEEE Trans Knowl Data Eng. 2007;19(4):453–67.CrossRefGoogle Scholar
  6. 6.
    Ester M, Kriegel HP, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd international conference on knowledge discovery and data mining. Menlo Park: AAAI Press; 1996. p. 226–231.Google Scholar
  7. 7.
    Froelich W, Wakulicz-Deja A. Mining temporal medical data using adaptive fuzzy cognitive maps. In: IEEE 2009 2nd conference on human system interactions; 2009. p. 16–23.Google Scholar
  8. 8.
    Halder S, Samiullah M, Lee YK. Supergraph based periodic pattern mining in dynamic social networks. Expert Syst Appl. 2017;72:430–42.CrossRefGoogle Scholar
  9. 9.
    Han J, Dong G, Yin Y. Efficient mining of partial periodic patterns in time series database. In: Proceedings of the 15th international conference on data engineering. Washington, DC: IEEE Computer Society; 1999. p. 106–115.Google Scholar
  10. 10.
    Huang KY, Chang CH. Mining periodic patterns in sequence data. In: Kambayashi Y, Mohania M, Wöß W, editors. Data warehousing and knowledge discovery. Berlin: Springer; 2004. p. 401–10.CrossRefGoogle Scholar
  11. 11.
    Ilayaraja M, Meyyappan T. Mining medical data to identify frequent diseases using apriori algorithm. In: IEEE 2013 international conference on pattern recognition, informatics and mobile engineering; 2013. p. 194–9.Google Scholar
  12. 12.
    Ismail WN, Hassan MM. Mining productive-associated periodic-frequent patterns in body sensor data for smart home care. Sensors. 2017;17(5):952.  https://doi.org/10.3390/s17050952.CrossRefGoogle Scholar
  13. 13.
    Ismail WN, Hassan MMAHAFG. Mining productive-periodic frequent patterns in tele-health systems. J Netw Comput Appl. 2018;115:33–47.  https://doi.org/10.1016/j.jnca.2018.04.014.CrossRefGoogle Scholar
  14. 14.
    Jindal T, Giridhar P, Tang LA, Li J, Han J. Spatiotemporal periodical pattern mining in traffic data. In: Proceedings of the 2nd ACM SIGKDD international workshop on urban computing (UrbComp ’13). New York: ACM; 2013. p. 11:1–11:8Google Scholar
  15. 15.
    Kullback S, Leibler RA. On information and sufficiency. Ann Math Stat. 1951;22(1):79–86.MathSciNetCrossRefGoogle Scholar
  16. 16.
    Li Z, Han J. Mining periodicity from dynamic and incomplete spatiotemporal data. Berlin: Springer; 2014. p. 41–81.Google Scholar
  17. 17.
    Li Z, Ding B, Han J, Kays R, Nye P. Mining periodic behaviors for moving objects. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining (KDD ’10). New York: ACM; 2010. p. 1099–1108.Google Scholar
  18. 18.
    Li Z, Han J, Ding B, Kays R. Mining periodic behaviors of object movements for animal and biological sustainability studies. Data Min Knowl Discov. 2011;24(2):355–86.MathSciNetCrossRefGoogle Scholar
  19. 19.
    Li Z, Han J, Ji M, Tang LA, Yu Y, Ding B, Lee JG, Kays R. Movemine: mining moving object data for discovery of animal movement patterns. ACM Trans Intell Syst Technol. 2011;2(4):37.CrossRefGoogle Scholar
  20. 20.
    Lomb NR. Least-squares frequency analysis of unequally spaced data. Astrophys Space Sci. 1976;39:447–62.CrossRefGoogle Scholar
  21. 21.
    Parthasarathy S, Mehta S, Srinivasan S. Robust periodicity detection algorithms. In: Proceedings of the 15th ACM international conference on information and knowledge management (CIKM ’06). New York: ACM; 2006. p. 874–5.Google Scholar
  22. 22.
    Pinaire J, Azé J, Bringay S, Landais P. Patient healthcare trajectory. An essential monitoring tool: a systematic review. Health Inf Sci Syst. 2017;5(1):1.CrossRefGoogle Scholar
  23. 23.
    Scargle JD. Studies in astronomical time series analysis. II—statistical aspects of spectral analysis of unevenly spaced data. Astrophys J. 1982;263:835–53.CrossRefGoogle Scholar
  24. 24.
    Sheng C, Hsu W, Lee ML. Mining dense periodic patterns in time series data. In: Proceedings of the 22nd international conference on data engineering. Washington, DC: IEEE Computer Society 2006. p. 115.Google Scholar
  25. 25.
    Vlachos M, Yu P, Castelli V. On periodicity detection and structural periodic similarity. In: Proceedings of the 5th SIAM international conference on data mining; 2005. p. 449–60.Google Scholar
  26. 26.
    Worton BJ. Kernel methods for estimating the utilization distribution in home-range studies. Ecology. 1989;70(1):164–8.CrossRefGoogle Scholar
  27. 27.
    Yang J, Wang W, Yu PS. Mining asynchronous periodic patterns in time series data. IEEE Trans Knowl Data Eng. 2003;15(3):613–28.CrossRefGoogle Scholar
  28. 28.
    Zhang M, Kao B, Cheung DW, Yip KY. Mining periodic patterns with gap requirement from sequences. ACM Trans Knowl Discov Data. 2007;1(2):7.CrossRefGoogle Scholar
  29. 29.
    Zhang D, Lee K, Lee I. Hierarchical trajectory clustering for spatio-temporal periodic pattern mining. Expert Syst Appl. 2018;92:1–11.CrossRefGoogle Scholar
  30. 30.
    Zhang D, Lee K, Lee I. Semantic periodic pattern mining from spatio-temporal trajectories. Inf Sci. Submitted 2018.Google Scholar
  31. 31.
    Zhou S, Ogihara A, Nishimura S, Jin Q. Analyzing the changes of health condition and social capital of elderly people using wearable devices. Health Inf Sci Syst. 2018;6(1):4.CrossRefGoogle Scholar
  32. 32.
    Zhu YL, Li SJ, Bao NN, Wan DS. Mining approximate periodic pattern in hydrological time series. In: Abbasi A, Giesen N, editors. EGU general assembly conference abstracts. vol. 14, 2012; p. 515.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Science & Information Technology Academy, Division of Tropical Environments & SocietiesJames Cook UniversityCairnsAustralia

Personalised recommendations