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Evaluation of automatic and manual knowledge acquisition for cerebrospinal fluid (CSF) diagnosis

  • A. Ultsch
  • T. O. Kleine
  • D. Korus
  • S. Farsch
  • G. Guimarães
  • W. Pietzuch
  • J. Simon
Knowledge Acquisition and Learning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1211)

Abstract

Neural Network technology for knowledge acquisition appears to be promising. Neural Networks alone, however, are insufficient for a medical domain, because of their inability to explain decisions. Knowledge conversion procedures from neural networks to symbolic knowledge are necessary. As a first milestone of the MEDWIS project NELA a knowledge conversion algorithm, called sig*, was tested. We compared a knowledge base generated by sig* with a first prototype of a manually built knowledge base for the diagnosis of cerebrospinal fluid. We have found the performance of the automatically build knowledge base to be quantitatively comparable to the manually acquired knowledge base. The quality of the extracted rules with respect to human understanding and medical plausibility was evaluated by use of a questionnaire. A low number of missing parameters found together with a correct choice of parameters in most cases indicated that the extracted rules correspond well to knowledge expressed by a medical expert. The results presented here confirm that a part of our automatic knowledge acquisition procedure, that is conversion of knowledge from subsymbolic representation to a rule based representation of knowledge, performed comparably, proved to be superior to manual knowledge acquisition with some respects.

Keywords

Knowledge Base Knowledge Acquisition Laboratory Diagnosis Acute Bacterial Meningitis Symbolic Knowledge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • A. Ultsch
    • 1
  • T. O. Kleine
    • 2
  • D. Korus
    • 1
  • S. Farsch
    • 1
  • G. Guimarães
    • 1
  • W. Pietzuch
    • 2
  • J. Simon
    • 2
  1. 1.Neuroinformatics and Artificial Intelligence, Dep. of MathematicsUniversity of MarburgMarburgGermany
  2. 2.Dep. of NeurochemistryMedical Centre of Nervous Diseases, University of MarburgMarburgGermany

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