Linked Life Data

21685609

Source:http://linkedlifedata.com/resource/pubmed/id/21685609

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2011-6-20
pubmed:abstractText
The majority of questions that arise in the practice of medicine relate to drug information. Additionally, adverse reactions account for as many as 98,000 deaths per year in the United States. Adverse drug reactions account for a significant portion of those errors. Many authors believe that clinical decision support associated with computerized physician order entry has the potential to decrease this adverse drug event rate. This decision support requires knowledge to drive the process. One important and rich source of drug knowledge is the DailyMed product labels. In this project we used computationally extracted SNOMED CT™ codified data associated with each section of each product label as input to a rules engine that created computable assertional knowledge in the form of semantic triples. These are expressed in the form of "Drug" HasIndication "SNOMED CT™". The information density of drug labels is deep, broad and quite substantial. By providing a computable form of this information content from drug labels we make these important axioms (facts) more accessible to computer programs designed to support improved care.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
T
pubmed:status
MEDLINE
pubmed:issn
0926-9630
pubmed:author
pubmed:issnType
Print
pubmed:volume
166
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
38-47
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Drug knowledge expressed as computable semantic triples.
pubmed:affiliation
Mount Sinai School of Medicine. ontolimatics@gmail.com
pubmed:publicationType
Journal Article