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)


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.


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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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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