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Automatic knowledge acquisition from medical texts

U Hahn1, K Schnattinger, M Romacker

  • 1Text Knowledge Engineering Lab, Freiburg University, Germany.

Proceedings : a Conference of the American Medical Informatics Association. AMIA Fall Symposium
|January 1, 1996
PubMed
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This study presents a knowledge-based approach for understanding medical texts, specifically gastro-intestinal disease findings. The developed parser generates text knowledge bases, enabling automatic knowledge acquisition from medical narratives.

Area of Science:

  • Medical Informatics
  • Computational Linguistics
  • Knowledge Representation

Background:

  • Understanding complex medical texts, particularly gastro-intestinal disease findings, presents significant challenges.
  • Existing methods may lack the integration of domain-specific knowledge for accurate interpretation.

Purpose of the Study:

  • To present a novel knowledge-based approach for the automated understanding of medical texts.
  • To detail the methodological features of a robust, incremental, and concurrent parsing model.

Main Methods:

  • Utilizing an object-oriented, fully lexicalized, dependency-based grammar model.
  • Integrating the grammar model with domain knowledge representations based on description logics.
  • Employing a parser designed for robustness, incrementality, and concurrency.

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Main Results:

  • The parser generates text knowledge bases from medical narratives.
  • These knowledge bases capture substantial portions of the content within medical documents.
  • The approach facilitates knowledge-based understanding of realistic medical texts.

Conclusions:

  • The proposed approach enables effective knowledge acquisition from medical texts.
  • The developed parser and knowledge representation system enhance the understanding of gastro-intestinal disease findings.
  • This method provides a foundation for building comprehensive medical knowledge bases.