NDLI: Data Mining to Generate Adverse Drug Events Detection Rules

Content Provider	IEEE Xplore Digital Library
Author	Chazard, E. Ficheur, G. Bernonville, S. Luyckx, M. Beuscart, R.
Copyright Year	1997
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.
Sponsorship	IEEE Engineering in Medicine and Biology Society IEEE Computer Society
Page Count	8
File Size	410984
Starting Page	823
Ending Page	830
File Format	PDF
ISSN	10897771
Volume Number	15
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Drugs Medical services Medical information systems Medical diagnostic imaging Data mining Patient monitoring Decision trees patient safety Adverse drug events (ADEs) data mining decision trees electronic health records
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications Biotechnology

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