Knowledge-Intensive Medical Process Similarity
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.
KeywordsDomain Knowledge Activity Node Edit Distance Brain Computerize Tomography Edit Operation
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.
- 1.Basu, R., Archer, N., Mukherjee, B.: Intelligent decision support in healthcare. Analytics 33–38, 2012 (2012)Google Scholar
- 5.LaRosa, M., Dumas, M., Uba, R., Dijkman, R.: Business process model merging: an approach to business process consolidation. ACM Trans. Softw. Eng. Methodol. 22(2), 11 (2013)Google Scholar
- 6.Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: a versatile graph matching algorithm and its application to schema matching. IEEE, USA (2002)Google Scholar
- 9.Montani, S., Leonardi, G., Quaglini, S., Baudi, A.: Improving process model retrieval by accounting for gateway nodes: an ongoing work. In: volume 1101 of CEUR Workshop Proceedings, pp. 31–40. CEUR-WS.org (2013)
- 16.Weijters, A., Van der Aalst, W., Alves de Medeiros, A.: Process Mining with the Heuristic Miner Algorithm, WP 166. Eindhoven University of Technology, Eindhoven (2006)Google Scholar