Advertisement

Finding Motifs in Medical Data

  • Vasily Osipov
  • Alexander Vodyaho
  • Elena Stankova
  • Nataly ZukovaEmail author
  • Bassel Zeno
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10408)

Abstract

Nowadays big volumes of medical data are accumulated. So the problem of analysis of these data and mining linked logical structures, defining internal data semantics is an actual one. Solution of this problem allows solve the problem of optimizing intelligent context search. In the article an approach for solving this problem for analyzing processes running in human organism is discussed. Suggested approach is based on building of linked logical structures and assumes finding of motifs in variations of parameters of systems and subsystems. An algorithm of finding of motifs is suggested. The result of algorithm operation is logical structure that reflects internal dependencies which exist in human organism. Nowadays suggested approach is used in Almazov Cardiological Center for medical data processing.

Keywords

Medical data Linked logical structure Motifs Context search 

Notes

Acknowledgement

This work was partially financially supported by Government of Russian Federation, Grant 074-U01.

References

  1. 1.
    Li, N., Crane, M., Gurrin, C., Ruskin, H.J.: Finding motifs in larger personal lifelogs. In: 7th Augmented Human International Conference 2016, 25–26 February 2016, Geneva, Switzerland (2016)Google Scholar
  2. 2.
    Kamath, C., Fan, Y.J.: Finding motifs in wind generation time series data. In: 2012 11th International Conference on Machine Learning and Applications (ICMLA), vol. 2, pp. 481–486, December 2012Google Scholar
  3. 3.
    Minnen, D., Isbell, C.L., Essa, I., Starner, T.: Discovering Multivariate Motifs using Subsequence Density Estimation and Greedy Mixture Learning (2007). http://www.cc.gatech.edu/~isbell/classes/reading/papers/minnen-aaai2007.pdf
  4. 4.
    Phuoc, N., Truyen, T., Wickramasinghe, N., Venkatesh, S.: Deepr: A Convolutional Net for Medical Records (2016). https://arxiv.org/pdf/1607.07519v1.pdf
  5. 5.
    Cressie, N., Read, T.: Pearson’s χ2 and the log-likelihood ratio statistic g2: a comparative review. Int. Stat. Rev 57(1), 19–43 (1989)Google Scholar
  6. 6.
    Baglivo, J., Olivier, D., Pagano, M.: Methods for exact goodness-of-fit tests. J. Am. Stat. Assoc. 87(418), 464–469 (1992)Google Scholar
  7. 7.
    Bailey, T.L., Elkan, C.: Fitting a mixture model by expectation maximization to discover motifs in biopolymers. In: Proceedings of International Conference Intelligent Systems for Molecular Biology, vol. 2, pp. 28–36 (1994)Google Scholar
  8. 8.
    Keich, U.: Efficiently computing the p-value of the entropy score. J. Comput. Biol. 12(4), 416–430 (2005)Google Scholar
  9. 9.
    Nagarajan, N.: Statistical techniques for biological motif discovery. Cornell University (2007)Google Scholar
  10. 10.
    Lawrence, C.E., Altschul, S.F., Boguski, M.S., Liu, J.S., Neuwald, A.F., Wootton. J.C.: Detecting subtle sequence signals: a gibbs sampling strategy for multiple alignment. Sci. New Ser. 262(5131), pp. 208–214 (1993)Google Scholar
  11. 11.
    Broomhead, D.S., King, G.P.: Extracting qualitative dynamics from experimental data. Phys. D. 20, 217–236 (1986)Google Scholar
  12. 12.
    Golyandina, N., Nekrutkin, V., Zhigljavsky, A.: Analysis of Time Series Structure: SSA and Related Techniques. Chapman and Hall/CRC (2001)Google Scholar
  13. 13.
    Gomes, E.F., Jorge, A.M., Azevedo, P.J.: Classifying heart sounds using sax motifs. random forests and text mining techniques. In: Proceedings of the 18th International Database Engineering & Applications (2001). http://repositorium.sdum.uminho.pt/bitstream/1822/33769/1/2001.pdf
  14. 14.
    Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding motifs in time series. In: Proceedings of the 2nd SigKDD Workshop on Temporal Data Mining 2002, pp. 53–68 (2002)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Vasily Osipov
    • 1
  • Alexander Vodyaho
    • 2
  • Elena Stankova
    • 3
  • Nataly Zukova
    • 2
    • 4
    Email author
  • Bassel Zeno
    • 4
  1. 1.St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS)St. PetersburgRussia
  2. 2.St. Petersburg State Electrotechnical UniversitySt. PetersburgRussia
  3. 3.St. Petersburg State UniversitySt. PetersburgRussia
  4. 4.St. Petersburg National Research University of Information Technologies, Mechanics and OpticsSt. PetersburgRussia

Personalised recommendations