title: Improving structural medical process comparison by exploiting domain knowledge and mined information.
abstract: OBJECTIVE
  Process model comparison and similar process retrieval is a key issue to be addressed in many real-world situations, and a particularly relevant one in medical applications, where similarity quantification can be exploited to accomplish goals such as conformance checking, local process adaptation analysis, and hospital ranking. In this paper, we present a framework that allows the user to: (i) mine the actual process model from a database of process execution traces available at a given hospital; and (ii) compare (mined) process models. The tool is currently being applied in stroke management.
  
  
  METHODS
  Our framework relies on process mining to extract process-related information (i.e., process models) from data. As for process comparison, we have modified a state-of-the-art structural similarity metric by exploiting: (i) domain knowledge; (ii) process mining outputs and statistical temporal information. These changes were meant to make the metric more suited to the medical domain.
  
  
  RESULTS
  Experimental results showed that our metric outperforms the original one, and generated output closer than that provided by a stroke management expert. In particular, our metric correctly rated 11 out of 15 mined hospital models with respect to a given query. On the other hand, the original metric correctly rated only 7 out of 15 models. The experiments also showed that the framework can support stroke management experts in answering key research questions: in particular, average patient improvement decreased as the distance (according to our metric) from the top level hospital process model increased.
  
  
  CONCLUSIONS
  The paper shows that process mining and process comparison, through a similarity metric tailored to medical applications, can be applied successfully to clinical data to gain a better understanding of different medical processes adopted by different hospitals, and of their impact on clinical outcomes. In the future, we plan to make our metric even more general and efficient, by explicitly considering various methodological and technological extensions. We will also test the framework in different domains.
authors.affiliation: Dipartimento di Scienze e Innovazione Tecnologica, Computer Science Institute, Università del Piemonte Orientale, Viale Michel 11, I-15121 Alessandria, Italy. Electronic address: stefania.montani@unipmn.it.
authors.forename: Stefania
authors.initials: S
authors.lastname: Montani
authors.affiliation: Dipartimento di Scienze e Innovazione Tecnologica, Computer Science Institute, Università del Piemonte Orientale, Viale Michel 11, I-15121 Alessandria, Italy.
authors.forename: Giorgio
authors.initials: G
authors.lastname: Leonardi
authors.affiliation: Department of Electrical, Computer and Biomedical Engineering, Università di Pavia, Via Ferrata 1, I-27100 Pavia, Italy.
authors.forename: Silvana
authors.initials: S
authors.lastname: Quaglini
authors.affiliation: Istituto di Ricovero e Cura a Carattere Scientifico Fondazione "C. Mondino", Via Mondino 2, I-27100 Pavia, Italy(1).
authors.forename: Anna
authors.initials: A
authors.lastname: Cavallini
authors.affiliation: Istituto di Ricovero e Cura a Carattere Scientifico Fondazione "C. Mondino", Via Mondino 2, I-27100 Pavia, Italy(1).
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Micieli
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D019468:Disease Management; D006801:Humans; D051188:Knowledge Bases; D017064:Process Assessment, Health Care / Q000379:methods*; D020521:Stroke / Q000628:therapy*
keywords: Graph edit distance; Process mining and comparison; Stroke management