title: Detection of adverse drug events detection: data aggregation and data mining.
abstract: Adverse drug events (ADEs) are a public health issue. The objective of this work is to data-mine electronic health records in order to automatically identify ADEs and generate alert rules to prevent those ADEs. The first step of data-mining is to transform native and complex data into a set of binary variables that can be used as causes and effects. The second step is to identify cause-to-effect relationships using statistical methods. After mining 10,500 hospitalizations from Denmark and France, we automatically obtain 250 rules, 75 have been validated till now. The article details the data aggregation and an example of decision tree that allows finding several rules in the field of vitamin K antagonists.
authors.affiliation: Lille university hospital, EA2694, Lille, France. emmanuel@chazard.org
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Chazard
authors.forename: Grégoire
authors.initials: G
authors.lastname: Ficheur
authors.forename: Béatrice
authors.initials: B
authors.lastname: Merlin
authors.forename: Michael
authors.initials: M
authors.lastname: Genin
authors.forename: Cristian
authors.initials: C
authors.lastname: Preda
authors.forename: Régis
authors.initials: R
authors.lastname: Beuscart
mesh_terms: D003625:Data Collection; D057225:Data Mining / Q000379:methods*; D003718:Denmark; D064420:Drug-Related Side Effects and Adverse Reactions / Q000175:diagnosis*; D005602:France; D006801:Humans; D016347:Medical Records Systems, Computerized