Knowledge-Intensive Medical Process Similarity

  • Stefania MontaniEmail author
  • Giorgio Leonardi
  • Silvana Quaglini
  • Anna Cavallini
  • Giuseppe Micieli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8903)


Process model comparison and similar processes retrieval are key issues to be addressed in many real world situations, and particularly relevant ones in medical applications, where similarity quantification can be exploited to accomplish goals such as conformance checking, local process adaptation analysis, and hospital ranking.

In recent years, we have implemented a framework which allows to: (i) extract the actual process model from the available process execution traces, through process mining techniques; and (ii) compare (mined) process models, by relying on a novel distance measure. Our distance measure is knowledge-intensive, in the sense that it explicitly makes use of domain knowledge, and can be properly adapted on the basis of the available knowledge representation formalism. We also exploit all the available mined information (e.g., temporal information about delays between activities). Interestingly, our metric explicitly takes into account complex control flow information too, which is often neglected in the literature.

The framework has been successfully tested in stroke management.


Domain Knowledge Activity Node Edit Distance Brain Computerize Tomography Edit Operation 
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.



We would like to thank Dr. I. Canavero for her independent work in the experimental phase.

This research is partially supported by the GINSENG Project, Compagnia di San Paolo.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Stefania Montani
    • 1
    Email author
  • Giorgio Leonardi
    • 1
  • Silvana Quaglini
    • 2
  • Anna Cavallini
    • 3
  • Giuseppe Micieli
    • 3
  1. 1.Dipartimento di Scienze e Innovazione Tecnologica, Computer Science InstituteUniversità del Piemonte OrientaleAlessandriaItaly
  2. 2.Department of Electrical, Computer and Biomedical EngineeringUniversità di PaviaPaviaItaly
  3. 3.on behalf of the Stroke Unit Network (SUN) collaborating centersIstituto di Ricovero e Cura a Carattere Scientifico Fondazione “C. Mondino”PaviaItaly

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