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Related Experiment Videos

UMLS concept indexing for production databases: a feasibility study.

P Nadkarni1, R Chen, C Brandt

  • 1Yale University School of Medicine, New Haven, CT 06520-8009, USA. Prakash.Nadkarni@yale.edu

Journal of the American Medical Informatics Association : JAMIA
|January 6, 2001
PubMed
Summary
This summary is machine-generated.

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Using the Unified Medical Language System (UMLS) Metathesaurus for medical concept identification in text proved feasible but had a high error rate. Further curation is needed for reliable concept indexing from medical narratives.

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Automated concept identification from medical narratives is crucial for efficient indexing.
  • The Unified Medical Language System (UMLS) Metathesaurus offers a comprehensive resource for medical terminology.

Purpose of the Study:

  • To assess the feasibility of using the UMLS Metathesaurus for computational identification of medical concepts in narrative text.
  • To quantitatively evaluate the accuracy of this strategy, measuring true positives, false positives, and false negatives.

Main Methods:

  • A concept-identification program was developed and tested using the 1999 UMLS Metathesaurus.
  • The program processed training and test sets of medical documents (discharge summaries, surgical notes).

Related Experiment Videos

  • Manual analysis and iterative refinement of the algorithm and UMLS subset were performed.

    • Main Results:

    • The concept identification strategy achieved true positive rates of 82.6% on the training set and 76.3% on the test set.
    • Identified issues included redundant concepts, homonyms, acronyms, missing concepts, proper names, and spelling errors.
    • These errors indicate limitations for direct production-mode concept indexing.

    Conclusions:

    • The current error rate is too high for the UMLS Metathesaurus-based strategy to be the sole method for preprocessing medical narratives.
    • Significant curation and refinement of the UMLS subset are necessary for effective concept matching.