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rdf:type |
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lifeskim:mentions |
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pubmed:dateCreated |
2000-2-1
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pubmed:abstractText |
We compare the performance of four computerized methods in identifying chest x-ray reports that support acute bacterial pneumonia. Two of the computerized techniques are constructed from expert knowledge, and two learn rules and structure from data. The two machine learning systems perform as well as the expert constructed systems. All of the computerized techniques perform better than a baseline keyword search and a lay person, and perform as well as a physician. We conclude that machine learning can be used to identify chest x-ray reports that support pneumonia.
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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 |
216-20
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pubmed:dateRevised |
2009-11-18
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pubmed:meshHeading |
pubmed-meshheading:10566352-Algorithms,
pubmed-meshheading:10566352-Artificial Intelligence,
pubmed-meshheading:10566352-Bayes Theorem,
pubmed-meshheading:10566352-Decision Support Systems, Clinical,
pubmed-meshheading:10566352-Evaluation Studies as Topic,
pubmed-meshheading:10566352-Expert Systems,
pubmed-meshheading:10566352-Humans,
pubmed-meshheading:10566352-Natural Language Processing,
pubmed-meshheading:10566352-Neural Networks (Computer),
pubmed-meshheading:10566352-Pneumonia, Bacterial,
pubmed-meshheading:10566352-Radiographic Image Interpretation, Computer-Assisted,
pubmed-meshheading:10566352-Radiography, Thoracic
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pubmed:year |
1999
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pubmed:articleTitle |
Comparing expert systems for identifying chest x-ray reports that support pneumonia.
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pubmed:affiliation |
Department of Medical Informatics, University of Utah, Salt Lake City 84132, USA.
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pubmed:publicationType |
Journal Article,
Comparative Study,
Research Support, U.S. Gov't, P.H.S.
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