Linked Life Data

17276736

Source:http://linkedlifedata.com/resource/pubmed/id/17276736

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2007-9-3
pubmed:abstractText
SNOMED is one of the leading health care terminologies being used worldwide. As such, quality assurance is an important part of its maintenance cycle. Methodologies for auditing SNOMED based on structural aspects of its organization are presented. In particular, automated techniques for partitioning SNOMED into smaller groups of concepts based primarily on relationships patterns are defined. Two abstraction networks, the area taxonomy and p-area taxonomy, are derived from the partitions. The high-level views afforded by these abstraction networks form the basis for systematic auditing. The networks tend to highlight errors that manifest themselves as irregularities at the abstract level. They also support group-based auditing, where sets of purportedly similar concepts are focused on for review. The auditing methodologies are demonstrated on one of SNOMED's top-level hierarchies. Errors discovered during the auditing process are reported.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1532-0480
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
40
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
561-81
pubmed:dateRevised
2008-10-23
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Structural methodologies for auditing SNOMED.
pubmed:affiliation
Computer Science Department, New Jersey Institute of Technology, University Heights, Newark, NJ 07102-1982, USA.
pubmed:publicationType
Journal Article