Mining Healthcare Data with Temporal Association Rules: Improvements and Assessment for a Practical Use

  • Stefano Concaro
  • Lucia Sacchi
  • Carlo Cerra
  • Pietro Fratino
  • Riccardo Bellazzi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5651)


The Regional Healthcare Agency (ASL) of Pavia has been maintaining a central data repository which stores healthcare data about the population of Pavia area. The analysis of such data can be fruitful for the assessment of healthcare activities. Given the crucial role of time in such databases, we developed a general methodology for the mining of Temporal Association Rules on sequences of hybrid events. In this paper we show how the method can be extended to suitably manage the integration of both clinical and administrative data. Moreover, we address the problem of developing an automated strategy for the filtering of output rules, exploiting the taxonomy underlying the drug coding system and considering the relationships between clinical variables and drug effects. The results show that the method could find a practical use for the evaluation of the pertinence of the care delivery flow for specific pathologies.


Temporal data mining temporal association rules hybrid events healthcare data diabetes mellitus 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Stefano Concaro
    • 1
    • 2
  • Lucia Sacchi
    • 1
  • Carlo Cerra
    • 2
  • Pietro Fratino
    • 3
  • Riccardo Bellazzi
    • 1
  1. 1.Dipartimento di Informatica e SistemisticaUniversità di PaviaItaly
  2. 2.Sistema Informativo Aziendale e Controllo di Gestione, ASL di PaviaItaly
  3. 3.Università di PaviaItaly

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