High-specificity neurological localization using a connectionist model

Stanley Tuhrim, James A. Reggia, Yun Peng

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Most previous connectionist models for diagnosis have been developed using error backpropagation. While these systems function reasonably well, they have been limited by their need for a large database of test cases, to situations where a single disorder is present, and by the large number of connections required between fully-connected sets of processing units. Here we describe a recently developed connectionist model that overcomes these limitations. This approach can reuse existing causal knowledge bases, works well in situations where multiple disorders can occur simultaneously, and does not require fully-connected sets of processing units. We demonstrate that the accuracy of this model is comparable to that of more conventional AI programs using the same knowledge base in determining precisely the site of brain damage in a group of 50 stroke patients. These results support the conclusion that connectionist models can effectively use pre-existing causal knowledge bases from AI systems, and that they can function accurately when handling actual clinical problems.

Original languageEnglish
Pages (from-to)521-532
Number of pages12
JournalArtificial Intelligence in Medicine
Volume6
Issue number6
DOIs
StatePublished - Dec 1994

Keywords

  • Competitive activation
  • Connectionist model
  • Diagnostic reasoning
  • Neural network
  • Neurological localization
  • Parsimonious covering

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