Linked Life Data

20841784

Source:http://linkedlifedata.com/resource/pubmed/id/20841784

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 1
pubmed:dateCreated
2010-9-15
pubmed:abstractText
With the rapidly growing use of electronic health records, the possibility of large-scale clinical information extraction has drawn much attention. We aim to extract adverse drug events and effects from records. As the first step of this challenge, this study assessed (1) how much adverse-effect information is contained in records, and (2) automatic extracting accuracy of the current standard Natural Language Processing (NLP) system. Results revealed that 7.7% of records include adverse event information, and that 59% of them (4.5% in total) can be extracted automatically. This result is particularly encouraging, considering the massive amounts of records, which are increasing daily.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
T
pubmed:status
MEDLINE
pubmed:issn
0926-9630
pubmed:author
pubmed:issnType
Print
pubmed:volume
160
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
739-43
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Extraction of adverse drug effects from clinical records.
pubmed:affiliation
Center for Knowledge Structuring, University of Tokyo, University of Tokyo Hospital, Tokyo, Japan. eiji.aramaki@gmail.com
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't