title: Improving risk-stratification of Diabetes complications using temporal data mining.
abstract: To understand which factor trigger worsened disease control is a crucial step in Type 2 Diabetes (T2D) patient management. The MOSAIC project, funded by the European Commission under the FP7 program, has been designed to integrate heterogeneous data sources and provide decision support in chronic T2D management through patients' continuous stratification. In this work we show how temporal data mining can be fruitfully exploited to improve risk stratification. In particular, we exploit administrative data on drug purchases to divide patients in meaningful groups. The detection of drug consumption patterns allows stratifying the population on the basis of subjects' purchasing attitude. Merging these findings with clinical values indicates the relevance of the applied methods while showing significant differences in the identified groups. This extensive approach emphasized the exploitation of administrative data to identify patterns able to explain clinical conditions.
authors.forename: Lucia
authors.initials: L
authors.lastname: Sacchi
authors.forename: Arianna
authors.initials: A
authors.lastname: Dagliati
authors.forename: Daniele
authors.initials: D
authors.lastname: Segagni
authors.forename: Paola
authors.initials: P
authors.lastname: Leporati
authors.forename: Luca
authors.initials: L
authors.lastname: Chiovato
authors.forename: Riccardo
authors.initials: R
authors.lastname: Bellazzi
mesh_terms: D057225:Data Mining / Q000379:methods*; D048909:Diabetes Complications / Q000209:etiology*; D003924:Diabetes Mellitus, Type 2 / Q000150:complications* / Q000628:therapy; D004363:Drug Utilization / Q000706:statistics & numerical data; D006801:Humans; D010593:Pharmaceutical Services / Q000706:statistics & numerical data; D010604:Pharmacy / Q000706:statistics & numerical data; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors