Linked Life Data

20841862

Source:http://linkedlifedata.com/resource/pubmed/id/20841862

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 2
pubmed:dateCreated
2010-9-15
pubmed:abstractText
To implement a knowledge-based clinical decision support system for clinical information systems, it is crucial to verify and validate the knowledge base. This study developed and tested the hypertension management CDSS, named LIGHT. This study used a knowledge representation framework based on SAGE and developed a knowledge converter to translate knowledge encoded into the knowledge engine. To verify knowledge converted through the knowledge converter that is included in the knowledge representation framework, expected recommendations were made according to the knowledge encoded based on 201 test cases. The expected recommendations were compared to those generated by the knowledge engine. To validate the knowledge base, two physicians reviewed the test cases and made medication orders according to the knowledge base. These medication orders were compared to recommendations generated by the LIGHT. The concordance rates for compelling indication and absolute contraindication were 85% and 100%, respectively. Another senior physician reviewed and analyzed the discrepancy cases between the orders of the two other physicians and system recommendations. Accordingly, the authors conclude that the knowledge base for hypertension management became more accurate and practical through the testing process.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
T
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
0926-9630
pubmed:author
pubmed:issnType
Print
pubmed:volume
160
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1140-4
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Verification & validation of the knowledge base for the hypertension management CDSS.
pubmed:affiliation
Department of Nursing, Eulji university, Daejeon, Korea. flowhykim@gmail.com
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Validation Studies