Document Detail


An approach to knowledge base construction based on expert opinions.
MedLine Citation:
PMID:  15472757     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
OBJECTIVES: To describe, validate and demonstrate an approach for knowledge base construction based on expert opinions. METHODS: A knowledge base containing the frequency of occurrence of manifestations in epileptic seizures is constructed based on information provided by neurologists/epileptologists. The reliability of the responses is determined with the inter-rater intraclass correlation coefficient (ICC). If the ICC is not large enough the Spearman-Brown prophecy formula can be used to predict the number of additional experts. We propose a method to assess whether an additional expert provides information consistent with the already acquired data as well as a method to detect experts with deviating opinions. The power of the first method was determined. RESULTS: Data were collected for five seizure types. The ICCs determined from the responses for the various seizure types after inclusion of the additional experts was in all cases almost equal to 0.9, the target value. Yet one expert with diverging opinions concerning the frequency of occurrence of manifestations for different seizure types could be identified. Excluding this participant improved the reliability of the data. The power of the methods was good (> or =0.75). CONCLUSIONS: It is shown that human experts can provide reliable information about the frequency of occurrence of manifestations in epileptic seizures. In addition, the described approach correctly identified neurologists/epileptologists with both consistent and diverging opinions about the frequency of occurrence of manifestations in a number of seizure types.
Authors:
J F van Ast; J L Talmon; W O Renier; A Hasman
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't; Validation Studies    
Journal Detail:
Title:  Methods of information in medicine     Volume:  43     ISSN:  0026-1270     ISO Abbreviation:  Methods Inf Med     Publication Date:  2004  
Date Detail:
Created Date:  2004-10-08     Completed Date:  2004-12-22     Revised Date:  2007-11-15    
Medline Journal Info:
Nlm Unique ID:  0210453     Medline TA:  Methods Inf Med     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  427-32     Citation Subset:  IM    
Affiliation:
Department of Medical Informatics, University of Maastricht, Maastricht, The Netherlands. w.vanast@mi.unimaas.nl
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MeSH Terms
Descriptor/Qualifier:
Artificial Intelligence*
Confidence Intervals
Databases as Topic*
Decision Support Systems, Clinical
Epilepsy / classification*,  epidemiology,  pathology
Evidence-Based Medicine
Humans
Incidence
Internal Medicine
Observer Variation
Probability

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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