title: Detection of adverse drug events: proposal of a data model.
abstract: Our main objective is to detect adverse drug events (ADEs) in former hospital stays. As ADEs are rare, that supposes to screen thousands of electronic health records (EHRs). For that purpose, we need to define a data model that has two main objectives: (1) being able to describe hospital stays from various hospitals (2) being tuned so as to prepare the data mining process: as ADEs are not flagged in the datasets, the data model must be optimized for ADE detection. The article presents the phases of the design and the data model that results from this work. It is compatible with many hospitals. It deals with diagnoses, drug prescriptions, lab results and administrative information. It allows for data mining and ADE detection in EHRs.
authors.affiliation: Lille university hospital, EA2694, Lille, France. emmanuel@chazard.org
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Chazard
authors.forename: Béatrice
authors.initials: B
authors.lastname: Merlin
authors.forename: Grégoire
authors.initials: G
authors.lastname: Ficheur
authors.forename: Jean-Charles
authors.initials: JC
authors.lastname: Sarfati
authors.forename: Régis
authors.initials: R
authors.lastname: Beuscart
mesh_terms: D057225:Data Mining; D003661:Decision Support Techniques; D064420:Drug-Related Side Effects and Adverse Reactions / Q000175:diagnosis*; D057286:Electronic Health Records; D006801:Humans