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Data mining to generate adverse drug events detection rules

Data mining to generate adverse drug events detection rules

Authors :
Régis Beuscart
Grégoire Ficheur
Stéphanie Bernonville
Emmanuel Chazard
Michel Luyckx
Source :
IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society. 15(6)
Publication Year :
2011

Abstract

Adverse drug events (ADEs) are a public health is sue. Their detection usually relies on voluntary reporting or medical chart reviews. The objective of this paper is to automatically detect cases of ADEs by data mining. 115 447 complete past hospital stays are extracted from six French, Danish, and Bulgarian hospitals using a common data model including diagnoses, drug administrations, laboratory results, and free-text records. Different kinds of outcomes are traced, and supervised rule induction methods (decision trees and association rules) are used to discover ADE detection rules, with respect to time constraints. The rules are then filtered, validated, and reorganized by a committee of experts. The rules are described in a rule repository, and several statistics are automatically computed in every medical department, such as the confidence, relative risk, and median delay of outcome appearance. 236 validated ADE-detection rules are discovered; they enable to detect 27 different kinds of outcomes. The rules use a various number of conditions related to laboratory results, diseases, drug administration, and demographics. Some rules involve innovative conditions, such as drug discontinuations.

Details

ISSN :
15580032
Volume :
15
Issue :
6
Database :
OpenAIRE
Journal :
IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society
Accession number :
edsair.doi.dedup.....31f10b0037883237db0c00c5192cc9b8