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Automated knowledge extraction from the UMLS

Q Zeng1, J J Cimino

  • 1Department of Medical Informatics, Columbia University, New York, New York, USA.

Proceedings. AMIA Symposium
|February 3, 1999
PubMed
Summary
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This study automated disease-chemical knowledge extraction from UMLS MRCOC, finding it highly sensitive for disease-drug relationships. This information enhances electronic patient records with disease-specific views.

Area of Science:

  • Medical Informatics
  • Knowledge Extraction
  • Biomedical Data Mining

Background:

  • The Unified Medical Language System (UMLS) Co-occurrence table (MRCOC) contains valuable disease-chemical relationship data.
  • Automated methods are needed to efficiently extract and utilize this knowledge.

Purpose of the Study:

  • To develop and evaluate an automated method for extracting disease-chemical relationship knowledge from the UMLS MRCOC.
  • To assess the quality and sensitivity of UMLS MRCOC knowledge compared to other sources.

Main Methods:

  • Automated extraction of disease-chemical relationships from UMLS MRCOC.
  • Comparison with disease-lab chemical relationships from DXplain and MED.
  • Comparison with manually acquired disease-drug chemical relationships from medical literature.

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Main Results:

  • The automated method successfully extracted disease-chemical knowledge from UMLS MRCOC.
  • UMLS MRCOC knowledge demonstrated good sensitivity, particularly for disease-drug relationships.
  • Evaluations confirmed the utility of UMLS MRCOC data.

Conclusions:

  • Automated extraction from UMLS MRCOC is effective for disease-chemical knowledge.
  • UMLS MRCOC provides valuable and sensitive data for disease-drug relationships.
  • This knowledge can be applied to create disease-specific views in electronic patient records.