Temporal data mining for the quality assessment of hemodialysis services

Riccardo Bellazzi, Cristiana Larizza, Paolo Magni, Roberto Bellazzi

Research output: Contribution to journalArticlepeer-review


Objective: This paper describes the temporal data mining aspects of a research project that deals with the definition of methods and tools for the assessment of the clinical performance of hemodialysis (HD) services, on the basis of the time series automatically collected during hemodialysis sessions. Methods: 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, two new methods for association rule discovery and temporal rule discovery are applied to the time series. Such methods exploit several pre-processing techniques, comprising data reduction, multi-scale filtering and temporal abstractions. Results: We have analyzed the data of more than 5800 dialysis sessions coming from 43 different patients monitored for 19 months. The qualitative rules associating the outcome parameters and the measured variables were examined by the domain experts, which were able to distinguish between rules confirming available background knowledge and unexpected but plausible rules. Conclusion: The new methods proposed in the paper are suitable tools for knowledge discovery in clinical time series. Their use in the context of an auditing system for dialysis management helped clinicians to improve their understanding of the patients' behavior.

Original languageEnglish
Pages (from-to)25-39
Number of pages15
JournalArtificial Intelligence in Medicine
Issue number1
Publication statusPublished - May 2005


  • Hemodialysis
  • Rule discovery
  • Temporal abstractions
  • Temporal data mining

ASJC Scopus subject areas

  • Artificial Intelligence
  • Medicine(all)


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