Paper Info
Title
A meta-data model for knowledge in decision support systems.
Abstract
Clinical decision support such as alerts, reminders and guidance are driven by rules often distributed among a variety of applications in a healthcare information system. Due to the increasing size of rule bases, there is a growing need to manage this dispersed knowledge in an integrated environment. A system for management of executable clinical knowledge such as rules should (1) assist in the development and maintenance of rules throughout the rules' life-cycles, (2) support search and retrieval of rules in the knowledge base (e.g., rules for diabetes, rules created by a particular individual), and (3) facilitate the analyses of rules in the knowledge base (e.g., identify rules not updated in the last year). In order to create such a clinical knowledge management system it is necessary to model the meta-data of rules. There have been efforts to document meta-data about rules within the Arden Syntax Medical Logical Modules' project. However, the maintenance and library categories in that project allow mainly free-text information about a rule. We have created a comprehensive meta-data structure and taxonomy for describing clinical rules that supports the features of a knowledge management system. We also tested this model using a representative set of rules.
Year
Venue
Keywords
2003
AMIA
artificial intelligence,classification
Field
DocType
ISSN
Decision analysis,Management information systems,Intelligent decision support system,Computer science,Decision support system,Knowledge management,Artificial intelligence,Clinical decision support system,R-CAST,Evidential reasoning approach,Machine learning,Decision engineering
Conference
1942-597X
Citations 
PageRank 
References 
2
0.43
0
Authors
5
Name
Order
Citations
PageRank
Yaron Denekamp11087.20
Aziz A. Boxwala258572.72
Gilad J. Kuperman328978.17
Blackford Middleton4916112.88
Robert Greenes5644106.18