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Quality Assessment of Hemodialysis Services through Temporal Data Mining

  • Riccardo Bellazzi
  • Cristiana Larizza
  • Paolo Magni
  • Roberto Bellazzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2780)

Abstract

This paper describes a research project that deals with the definition of methods and tools for the assessment of the clinical performance of a hemodialysis service on the basis of time series data automatically collected during the monitoring of hemodialysis sessions. While simple statistical summaries are computed to assess basic outcomes, Intelligent Data Analysis and Temporal Data mining techniques are applied to gain insight and to discover knowledge on the causes of unsatisfactory clinical results. In particular, different techniques, comprising multi-scale filtering, Temporal Abstractions, association rules discovery and subgroup discovery are applied on the time series. The paper describes the application domain, the basic goals of the project and the methodological approach applied for time series data analysis. The current results of the project, obtained on the data coming from more than 2500 dialysis sessions of 33 patients monitored for seven months, are also shown.

Keywords

Association Rule Dialysis Session Subgroup Discovery Cardiac Frequency Temporal Abstraction 
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.

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References

  1. 1.
    Stefanelli, M.: The socio-organizational age of artificial intelligence in medicine. Artif. Intell. Med. 23, 25–47 (2001)CrossRefGoogle Scholar
  2. 2.
    Abidi, S.S.: Knowledge management in healthcare: towards ’knowledge-driven’ decisionsupport services. Int. J. Med. Inf. 63, 5–18 (2001)CrossRefGoogle Scholar
  3. 3.
    Zoccali, C.: Medical knowledge, quality of life and accreditation of quality in health care. The perspective of the clinical nephrologist. Int. J. Artif. Organs. 11, 717–720 (1998)Google Scholar
  4. 4.
    Roddick, J.F., Spiliopoulou, M.: A survey of temporal knowledge discovery paradigms and methods. IEEE T. Knowl. Data En. 14, 750–766 (2002)CrossRefGoogle Scholar
  5. 5.
    Registro Italiano di Dialisi e Trapianto, http://www.sin-italia.org
  6. 6.
    McFarlane, P.A., Mendelssohn, D.C.: A call to arms: economic barriers to optimal dialysis care. Perit. Dial. Int. 20, 7–12 (2000)Google Scholar
  7. 7.
    Moncrief, J.W.: Telemedicine in the care of the end-stage renal disease patients. Adv. Ren. Replace. Ther. 5, 286–291 (1998)Google Scholar
  8. 8.
    Ronco, C., Brendolan, A., Bellomo, R.: Online monitoring in continuous renal replacement therapies. Kidney Int. 56, 8–14 (1999)CrossRefGoogle Scholar
  9. 9.
    Bellazzi, R., Magni, P., Bellazzi, R.: Improving dialysis services through information technology: from telemedicine to data mining. Medinfo. 10(Pt 1), 795–799 (2001)Google Scholar
  10. 10.
    Shahar, Y.: A Framework for Knowledge-Based Temporal Abstraction. Art. Int. 90, 79–133 (1997)zbMATHCrossRefGoogle Scholar
  11. 11.
    Bellazzi, R., Larizza, C., Riva, A.: Temporal Abstractions for Interpreting Diabetic patients monitoring data. Intelligent Data Analysis 2, 97–122 (1998)CrossRefGoogle Scholar
  12. 12.
    Allen, J.F.: Towards a general theory of action and time. Artificial Intelligence 23, 123–154 (1984)zbMATHCrossRefGoogle Scholar
  13. 13.
    Cohen, A., Daubechies, I., Jawert, B., Vial, P.: Bioorthogonal basis of compactly supported wavelets. Comm. Pure Aplli. Math. 45, 485–560 (1992)zbMATHCrossRefGoogle Scholar
  14. 14.
    Höppner, F.: Discovery of Temporal Patterns - Learning Rules about the Qualitative Behaviour of Time Series. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 192–203. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  15. 15.
    Witten, I., Frank, E.: Data Mining. Academic Press, London (2000)Google Scholar
  16. 16.
    Gamberger, D., Lavrac, N.: Expert-guided subgroup discovery: Methodology and Application. J. Artif. Intell. Res. 17, 501–527 (2002)zbMATHGoogle Scholar
  17. 17.
    Gamberger, D., Šmuc, T.: Data Mining Server Rudjer Boskovic Institute, Laboratory for Information Systems. Zagreb, Croatia (2001), http://dms.irb.hr/

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Riccardo Bellazzi
    • 1
  • Cristiana Larizza
    • 1
  • Paolo Magni
    • 1
  • Roberto Bellazzi
    • 2
  1. 1.Dip. Informatica e SistemisticaUniversità di PaviaPaviaItaly
  2. 2.S.O Vigevano, A.O. PaviaUnità Operativa di Nefrologia e DialisiVigevanoItaly

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