title: Data-mining-based detection of adverse drug events.
abstract: Every year adverse drug events (ADEs) are known to be responsible for 98,000 deaths in the USA. Classical methods rely on report statements, expert knowledge, and staff operated record review. One of our objectives, in the PSIP project framework, is to use data mining (e.g., decision trees) to electronically identify situations leading to risk of ADEs. 10,500 hospitalization records from Denmark and France were used. 500 rules were automatically obtained, which are currently being validated by experts. A decision support system to prevent ADEs is then to be developed. The article examines a decision tree and the rules in the field of vitamin K antagonists.
authors.affiliation: Medical Information and Records Department EA2694, University Hospital, 59000 Lille, France. emmanuel@chazard.org
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Chazard
authors.forename: Cristian
authors.initials: C
authors.lastname: Preda
authors.forename: Béatrice
authors.initials: B
authors.lastname: Merlin
authors.forename: Grégoire
authors.initials: G
authors.lastname: Ficheur
authors.forename: Régis
authors.initials: R
authors.lastname: Beuscart
mesh_terms: D000925:Anticoagulants / Q000008:administration & dosage; D016208:Databases, Factual; D003663:Decision Trees; D064420:Drug-Related Side Effects and Adverse Reactions; D016247:Information Storage and Retrieval; D008490:Medical Informatics; D014812:Vitamin K / Q000037:antagonists & inhibitors
chemical_list: D000925:Anticoagulants; D014812:Vitamin K