Linked Life Data

19390100

Source:http://linkedlifedata.com/resource/pubmed/id/19390100

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2009-7-3
pubmed:abstractText
OBJECTIVE The authors developed a natural language processing (NLP) framework that could be used to extract clinical findings and diagnoses from dictated physician documentation. DESIGN De-identified documentation was made available by i2b2 Bio-informatics research group as a part of their NLP challenge focusing on obesity and its co-morbidities. The authors describe their approach, which used a combination of concept detection, context validation, and the application of a variety of rules to conclude patient diagnoses. RESULTS The framework was successful at correctly identifying diagnoses as judged by NLP challenge organizers when compared with a gold standard of physician annotations. The authors overall kappa values for agreement with the gold standard were 0.92 for explicit textual results and 0.91 for intuited results. The NLP framework compared favorably with those of the other entrants, placing third in textual results and fourth in intuited results in the i2b2 competition. CONCLUSIONS The framework and approach used to detect clinical conditions was reasonably successful at extracting 16 diagnoses related to obesity. The system and methodology merits further development, targeting clinically useful applications.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1067-5027
pubmed:author
pubmed:issnType
Print
pubmed:volume
16
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
585-9
pubmed:dateRevised
2010-9-27
pubmed:meshHeading
pubmed:articleTitle
Natural language processing framework to assess clinical conditions.
pubmed:affiliation
Medquist, Inc, Morgantown, WV, USA.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Evaluation Studies