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    Dynamic knowledge validation and verification for CBR teledermatology system

    Access Status
    Fulltext not available
    Authors
    Ou, Monica H.
    Lazarescu, Mihai
    West, Geoffrey
    Clay, C.
    Date
    2007
    Type
    Journal Article
    
    Metadata
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    Citation
    Ou, Monica H. and Lazarescu, Mihai and West, Geoffrey and Clay, Chris. 2007. Dynamic knowledge validation and verification for CBR teledermatology system. Artificial Intelligence in Medicine 39 (1): pp. 79-96.
    Source Title
    Artificial Intelligence in Medicine
    DOI
    10.1016/j.artmed.2006.08.004
    ISSN
    09333657
    Faculty
    Institute for Multi
    School of Electrical Engineering and Computing
    Department of Computing
    Faculty of Science and Engineering
    Sensor Processing and Content Analysis (Research Institute)
    School
    Institute for Multi- Sensor Processing & Content Analysis (Research Institute)
    Remarks

    The link to the journal’s home page is: http://www.elsevier.com/wps/find/journaldescription.cws_home/505627/description#description

    Copyright © 2007 Elsevier Ltd. All rights reserved

    URI
    http://hdl.handle.net/20.500.11937/30727
    Collection
    • Curtin Research Publications
    Abstract

    Objective: Case-based reasoning has been of great importance in the development of many decision support applications. However, relatively little effort has gone into investigating how new knowledge can be validated. Knowledge validation is important in dealing with imperfect data collected over time, because inconsistencies in data do occur and adversely affect the performance of a diagnostic system.Methods: This paper consists of two parts. First, it describes methods that enable the domain expert, who may not be familiar with machine learning, to interactively validate knowledge base of a Web-based teledermatology system. The validation techniques involve decision tree classification and formal concept analysis. Second, it describes techniques to discover unusual relationships hidden in the dataset for building and updating a comprehensive knowledge base, because the diagnostic performance of the system is highly dependent on the content thereof. Therefore, in order to classify different kinds of diseases, it is desirable to have a knowledge base that covers common as well as uncommon diagnoses.Results and conclusion: Evaluation results show that the knowledge validation techniques are effective in keeping the knowledge base consistent, and that the query refinement techniques are useful in improving the comprehensiveness of the case base.

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