Linked Life Data

14728261

Source:http://linkedlifedata.com/resource/pubmed/id/14728261

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2004-1-19
pubmed:abstractText
Detection and prevention of adverse events and, in particular, adverse drug events (ADEs), is an important problem in health care today. We describe the implementation and evaluation of four variations on the simple Bayes model for identifying ADE-related discharge summaries. Our results show that these probabilistic techniques achieve an ROC curve area of up to 0.77 in correctly determining which patient cases should be assigned an ADE-related ICD-9-CM code. These results suggest a potential for these techniques to contribute to the development of an automated system that helps identify ADEs, as a step toward further understanding and preventing them.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1942-597X
pubmed:author
pubmed:issnType
Electronic
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
689-93
pubmed:dateRevised
2010-9-20
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
Detecting adverse drug events in discharge summaries using variations on the simple Bayes model.
pubmed:affiliation
Center for Biomedical Informatics, University of Pittsburgh, Pennsylvania, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.