Linked Life Data

17238307

Source:http://linkedlifedata.com/resource/pubmed/id/17238307

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2007-1-22
pubmed:abstractText
A method for automatic analysis of time-oriented clinical narratives would be of significant practical import for medical decision making, data modeling and biomedical research. This paper proposes a robust corpus-based approach for temporal analysis of medical discharge summaries. We characterize temporal organization of clinical narratives in terms of temporal segments and their ordering. We consider a temporal segment to be a fragment of text that does not exhibit abrupt changes in temporal focus. Our method derives temporal order based on a range of linguistic and contextual features that are integrated in a supervised machine-learning framework. Our learning method achieves 83% F-measure in tempo-ral segmentation, and 78.3% accuracy in inferring pairwise temporal relations.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1942-597X
pubmed:author
pubmed:issnType
Electronic
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
81-5
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Finding temporal order in discharge summaries.
pubmed:affiliation
Massachusetts Institute of Technology (MIT), Cambridge, MA, USA.
pubmed:publicationType
Journal Article, Evaluation Studies, Research Support, N.I.H., Extramural