Flexible and Efficient Retrieval of Haemodialysis Time Series

  • Stefania Montani
  • Giorgio Leonardi
  • Alessio Bottrighi
  • Luigi Portinale
  • Paolo Terenziani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7738)


The problem of retrieving time series similar to a specified query pattern has been recently addressed within the Case Based Reasoning (CBR) literature. Providing a flexible and efficient way of dealing with such an issue would be of paramount importance in medical domains, where many patient parameters are often collected in the form of time series. In this paper, we describe a novel framework for retrieving cases with time series features, relying on Temporal Abstractions. With respect to more classical (mathematical) approaches, our framework provides significant advantages. In particular, multi-level abstraction mechanisms and proper indexing techniques allow for flexible query issuing, and for efficient and interactive query answering. The framework is currently being applied to the hemodialysis domain. In this field, experimental results have shown the superiority of our approach with respect to the use of a classical mathematical technique in flexibility, user friendliness, and also quality of results.

Tests in other application domains, as well as further enhancements, are foreseen in our future work.


Time Series Discrete Fourier Transform Index Structure Case Base Reasoning Query Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations and systems approaches. AI Communications 7, 39–59 (1994)Google Scholar
  2. 2.
    Agrawal, R., Faloutsos, C., Swami, A.: Efficient Similarity Search in Sequence Databases. In: Lomet, D.B. (ed.) FODO 1993. LNCS, vol. 730, pp. 69–84. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  3. 3.
    Bellazzi, R., Larizza, C., Magni, P., Montani, S., Stefanelli, M.: Intelligent analysis of clinical time series: an application in the diabetes mellitus domain. Artificial Intelligence in Medicine 20, 37–57 (2000)CrossRefGoogle Scholar
  4. 4.
    Bellazzi, R., Larizza, C., Riva, A.: Temporal abstractions for interpreting diabetic patients monitoring data. Intelligent Data Analysis 2, 97–122 (1998)CrossRefGoogle Scholar
  5. 5.
    Bergmann, R., Stahl, A.: Similarity Measures for Object-Oriented Case Representations. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS (LNAI), vol. 1488, pp. 25–36. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Bichindaritz, I., Conlon, E.: Temporal knowledge representation and organization for case-based reasoning. In: Proc. TIME 1996, pp. 152–159. IEEE Computer Society Press, Washington, DC (1996)Google Scholar
  7. 7.
    Branting, L.K., Hastings, J.D.: An empirical evaluation of model-based case matching and adaptation. In: Proc. Workshop on Case-Based Reasoning, AAAI 1994 (1994)Google Scholar
  8. 8.
    Chan, K.P., Fu, A.W.C.: Efficient time series matching by wavelets. In: Proc. ICDE 1999, pp. 126–133. IEEE Computer Society Press, Washington, DC (1999)Google Scholar
  9. 9.
    Combi, C., Pozzi, G., Rossato, R.: Querying temporal clinical databases on granular trends. Journal of Biomedical Informatics 45(2), 273–291 (2012)CrossRefGoogle Scholar
  10. 10.
    Daw, C.S., Finney, C.E., Tracy, E.R.: Symbolic analysis of experimental data. Review of Scientific Instruments (July 22, 2002) (2001)Google Scholar
  11. 11.
    Fuch, B., Mille, A., Chiron, B.: Operator Decision Aiding by Adaptation of Supervision Strategies. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS (LNAI), vol. 1010, pp. 23–32. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  12. 12.
    Funk, P., Xiong, N.: Extracting Knowledge from Sensor Signals for Case-Based Reasoning with Longitudinal Time Series Data. In: Perner, P. (ed.) Case-Based Reasoning in Signals and Images. SCI, vol. 73, pp. 247–284. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Hetland, M.L.: A survey of recent methods for efficient retrieval of similar time sequences. In: Last, M., Kandel, A., Bunke, H. (eds.) Data Mining in Time Series Databases. World Scientific, London (2003)Google Scholar
  14. 14.
    Inrig, J.K., Patel, U.D., Toto, R.D., Szczech, L.A.: Association of blood pressure increases during hemodialysis with 2-year mortality in incident hemodialysis patients: A secondary analysis of the dialysis morbidity and mortality wave 2 study. American Journal of Kidney Diseases 54(5), 881–890 (2009)CrossRefGoogle Scholar
  15. 15.
    Jaczynski, M.: A framework for the management of past experiences with time-extended situations. In: Proc. ACM Conference on Information and Knowledge Management (CIKM 1997), pp. 32–38. ACM Press, New York (1997)Google Scholar
  16. 16.
    Jære, M.D., Aamodt, A., Skalle, P.: Representing Temporal Knowledge for Case-Based Prediction. In: Craw, S., Preece, A. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 174–188. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  17. 17.
    Kadar, S., Wang, J., Showalter, K.: Noise-supported travelling waves in sub-excitable media. Nature 391, 770–772 (1998)CrossRefGoogle Scholar
  18. 18.
    Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems 3(3), 263–286 (2000)CrossRefGoogle Scholar
  19. 19.
    Keravnou, E.T.: Modeling Medical Concepts as Time Objects. In: Wyatt, J.C., Stefanelli, M., Barahona, P. (eds.) AIME 1995. LNCS (LNAI), vol. 934, pp. 67–90. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  20. 20.
    Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: Proc. of ACM-DMKD, San Diego (2003)Google Scholar
  21. 21.
    Ma, J., Knight, B.: A Framework for Historical Case-Based Reasoning. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 246–260. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  22. 22.
    Miksch, S., Horn, W., Popow, C., Paky, F.: Utilizing temporal data abstractions for data validation and therapy planning for artificially ventilated newborn infants. Artificial Intelligence in Medicine 8, 543–576 (1996)CrossRefGoogle Scholar
  23. 23.
    Montani, S., Bottrighi, A., Leonardi, G., Portinale, L.: A cbr-based, closed loop architecture for temporal abstractions configuration. Computational Intelligence 25(3), 235–249 (2009)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Montani, S., Portinale, L.: Accounting for the temporal dimension in case-based retrieval: a framework for medical applications. Computational Intelligence 22, 208–223 (2006)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Montani, S., Portinale, L., Leonardi, G., Bellazzi, R., Bellazzi, R.: Case-based retrieval to support the treatment of end stage renal failure patients. Artificial Intelligence in Medicine 37, 31–42 (2006)CrossRefGoogle Scholar
  26. 26.
    Nakhaeizadeh, G.: Learning Prediction from Time Series: A Theoretical and Empirical Comparison of CBR with some other Approaches. In: Wess, S., Richter, M., Althoff, K.-D. (eds.) EWCBR 1993. LNCS (LNAI), vol. 837, pp. 65–76. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  27. 27.
    Nilsson, M.: Retrieving short and dynamic biomedical sequences. In: Proc. 18th International Florida Artificial Intelligence Research Society Conference–Special Track on Case-Based Reasoning. AAAI Press (2005)Google Scholar
  28. 28.
    Nilsson, M., Funk, P., Olsson, E., von Scheele, B., Xiong, N.: Clinical decision-support for diagnosing stress-related disorders by applying psychophysiological medical knowledge to an instance-based learning system. Artificial Intelligence in Medicine 36, 159–176 (2006)CrossRefGoogle Scholar
  29. 29.
    Nilsson, M., Funk, P., Xiong, N.: Clinical decision support by time series classification using wavelets. In: Chen, C.S., Filipe, J., Seruca, I., Cordeiro, J. (eds.) Proc. Seventh International Conference on Enterprise Information Systems (ICEIS 2005), pp. 169–175. INSTICC Press (2005)Google Scholar
  30. 30.
    Palma, J., Juarez, J.M., Campos, M., Marin, R.: A fuzzy approach to temporal model-based diagnosis for intensive care units. In: Lopez de Mantaras, R., Saitta, L. (eds.) Proc. European Conference on Artificial Intelligence (ECAI 2004), pp. 868–872. IOS Press, Amsterdam (2004)Google Scholar
  31. 31.
    Portinale, L., Montani, S., Bottrighi, A., Leonardi, G., Juarez, J.: A case-based architecture for temporal abstraction configuration and processing. In: Proc. IEEE International Conference on Tools with Artificial Intelligent (ICTAI), pp. 667–674. IEEE Computer Society, Los Alamitos (2006)Google Scholar
  32. 32.
    Ram, A., Santamaria, J.C.: Continuous case-based reasoning. In: Proc. AAAI Case-Based Reasoning Workshop, pp. 86–93 (1993)Google Scholar
  33. 33.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proc. IJCAI, pp. 448–453 (1995)Google Scholar
  34. 34.
    Rougegrez, S.: Similarity Evaluation Between Observed Behaviours for the Prediction of Processes. In: Wess, S., Richter, M., Althoff, K.-D. (eds.) EWCBR 1993. LNCS (LNAI), vol. 837, pp. 155–166. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  35. 35.
    Shahar, Y.: A framework for knowledge-based temporal abstractions. Artificial Intelligence 90, 79–133 (1997)zbMATHCrossRefGoogle Scholar
  36. 36.
    Shahar, Y., Musen, M.A.: Knowledge-based temporal abstraction in clinical domains. Artificial Intelligence in Medicine 8, 267–298 (1996)CrossRefGoogle Scholar
  37. 37.
    Stacey, M.: Knowledge based temporal abstractions within the neonatal intesive care domain. In: Proc. CSTE Innovation Conference, University of Western Sidney (2005)Google Scholar
  38. 38.
    Terenziani, P., German, E., Shahar, Y.: The temporal aspects of clinical guidelines. In: Ten Teije, A., Miksch, S., Lucas, P. (eds.) Computer-based Medical Guidelines and Protocols: A Primer and Current Trends (2008)Google Scholar
  39. 39.
    Xia, B.B.: Similarity search in time series data sets. Technical report, School of Computer Science, Simon Fraser University (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stefania Montani
    • 1
  • Giorgio Leonardi
    • 1
  • Alessio Bottrighi
    • 1
  • Luigi Portinale
    • 1
  • Paolo Terenziani
    • 1
  1. 1.DISIT, Sezione di InformaticaUniversità del Piemonte OrientaleAlessandriaItaly

Personalised recommendations