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rdf:type |
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lifeskim:mentions |
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pubmed:dateCreated |
1999-3-16
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pubmed:abstractText |
Our natural language understanding system outputs a list of diseases, findings, and appliances found in a chest x-ray report. The system described in this paper links those diseases and findings that are causally related. Using Bayesian networks to model the conceptual and diagnostic information found in a chest x-ray we are able to infer more specific information about the findings that are linked to diseases.
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pubmed:grant |
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pubmed:commentsCorrections |
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pubmed:language |
eng
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pubmed:journal |
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pubmed:citationSubset |
IM
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pubmed:status |
MEDLINE
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pubmed:issn |
1531-605X
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pubmed:author |
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pubmed:issnType |
Print
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
587-91
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pubmed:dateRevised |
2009-11-18
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pubmed:meshHeading |
pubmed-meshheading:9929287-Algorithms,
pubmed-meshheading:9929287-Bayes Theorem,
pubmed-meshheading:9929287-Causality,
pubmed-meshheading:9929287-Diagnosis, Computer-Assisted,
pubmed-meshheading:9929287-Humans,
pubmed-meshheading:9929287-Models, Theoretical,
pubmed-meshheading:9929287-Natural Language Processing,
pubmed-meshheading:9929287-Radiography, Thoracic,
pubmed-meshheading:9929287-Semantics
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pubmed:year |
1998
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pubmed:articleTitle |
Bayesian modeling for linking causally related observations in chest X-ray reports.
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pubmed:affiliation |
University of Utah and LDS Hospital, Salt Lake City, USA.
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pubmed:publicationType |
Journal Article,
Research Support, U.S. Gov't, P.H.S.
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