title: Temporal data mining for the assessment of the costs related to diabetes mellitus pharmacological treatment.
abstract: Diabetes care and chronic disease management represent data-intensive contexts which allow Local Healthcare Agencies (ASL) to collect a huge amount of information. Time is often an essential component of such information, given the strong importance of the temporal evolution of the considered disease and of its treatment. In this paper we show the application of a temporal data mining technique to extract temporal association rules over an integrated repository including both administrative and clinical data related to a sample of diabetic patients. We will show how the method can be used to highlight cases and conditions which lead to the highest pharmaceutical costs. Considering the perspective of a Regional Healthcare Agency, this method could be properly exploited to assess the overall standards and quality of care, while lowering costs.
authors.affiliation: University of Pavia, Pavia, Italy.
authors.forename: Stefano
authors.initials: S
authors.lastname: Concaro
authors.forename: Lucia
authors.initials: L
authors.lastname: Sacchi
authors.forename: Carlo
authors.initials: C
authors.lastname: Cerra
authors.forename: Mario
authors.initials: M
authors.lastname: Stefanelli
authors.forename: Pietro
authors.initials: P
authors.lastname: Fratino
authors.forename: Riccardo
authors.initials: R
authors.lastname: Bellazzi
mesh_terms: D000368:Aged; D000465:Algorithms; D057225:Data Mining; D016208:Databases, Factual; D003920:Diabetes Mellitus / Q000188:drug therapy* / Q000191:economics; D016527:Drug Costs; D006801:Humans; D008875:Middle Aged; D055553:Prescription Drugs / Q000191:economics*
chemical_list: D055553:Prescription Drugs