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DIAGEN-WebDB: A Connectionist Approach to Medical Knowledge Representation and Inference

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Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence (IWANN 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2084))

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Abstract

In this paper we explore the relationships between symbolic and connectionist models of medical knowledge in the diagnosis task. First we reassume the motivations of the ground-work stages of connectionism. Then a relational network is obtained from the natural language description of the diagnosis task and subsequently this network is transformed into a connectionist one via the dual graph. Finally we comment on the symbiosis between symbolic and neural computation. The aim of the paper is to explore some of the similarities and differences between the two basic approach to artificial intelligence.

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References

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Author information

Authors and Affiliations

  1. Dpto. Inteligencia Artificial. Fac. Ciencias, UNED, Senda del Rey 9, 28040, Madrid, Spain

    José Mira, Rafael Martínez, José Ramón Álvarez & Ana E. Delgado

Authors
  1. José Mira
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  2. Rafael Martínez
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  3. José Ramón Álvarez
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  4. Ana E. Delgado
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Editor information

Editors and Affiliations

  1. Departamento de Inteligencia Artificial Sanda del Rey, Universidad Nacional de Educación a Distancia, s/n., Madrid, 28040, Spain

    José Mira

  2. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Campus Fuentenueva, 18071, Granada, Spain

    Alberto Prieto

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© 2001 Springer-Verlag Berlin Heidelberg

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Mira, J., Martínez, R., Álvarez, J.R., Delgado, A.E. (2001). DIAGEN-WebDB: A Connectionist Approach to Medical Knowledge Representation and Inference. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_93

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  • DOI: https://doi.org/10.1007/3-540-45720-8_93

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42235-8

  • Online ISBN: 978-3-540-45720-6

  • eBook Packages: Springer Book Archive

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