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Data mining to generate adverse drug events detection rules
Data mining to generate adverse drug events detection rules
- 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.
- Subjects :
- Decision support system
Association rule learning
Drug-Related Side Effects and Adverse Reactions
Decision tree
MEDLINE
computer.software_genre
Patient safety
Medicine
Adverse Drug Reaction Reporting Systems
Data Mining
Electronic Health Records
Humans
Electrical and Electronic Engineering
Medical diagnosis
Rule induction
business.industry
Medical record
Decision Trees
General Medicine
Decision Support Systems, Clinical
Computer Science Applications
Data mining
Patient Safety
business
computer
Software
Biotechnology
Subjects
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