Comparison of two knowledge bases on the detection of drug-drug interactions

This paper describes a drug ordering decision support system that helps with the prevention of adverse drug events by detecting drug-drug interactions in drug orders. The architecture of the system was devised in order to facilitate its use attached to physician order entry systems. The described mo...

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Bibliographic Details
Published in:Proceedings - AMIA Symposium 2000, p.171-175
Main Authors: Del Fiol, G, Rocha, B H, Kuperman, G J, Bates, D W, Nohama, P
Format: Article
Language:English
Subjects:
Artificial Intelligence
Decision Support Systems, Clinical
Drug Interactions
Drug Prescriptions
Drug Therapy, Computer-Assisted
Humans
Medication Systems, Hospital
Retrospective Studies
Online Access:Get full text
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Summary:This paper describes a drug ordering decision support system that helps with the prevention of adverse drug events by detecting drug-drug interactions in drug orders. The architecture of the system was devised in order to facilitate its use attached to physician order entry systems. The described model focuses in issues related to knowledge base maintenance and integration with external systems. Finally, a retrospective study was performed. Two knowledge bases, developed by different academic centers, were used to detect drug-drug interactions in a dataset with 37,237 drug prescriptions. The study concludes that the proposed knowledge base architecture enables content from other knowledge sources to be easily transferred and adapted to its structure. The study also suggests a method that can be used on the evaluation and refinement of the content of drug knowledge bases.
ISSN:1531-605X