Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2001 Jan-Feb;8(1):80-91.
doi: 10.1136/jamia.2001.0080080.

UMLS concept indexing for production databases: a feasibility study

P Nadkarni  1 , R Chen, C Brandt
Affiliations

UMLS concept indexing for production databases: a feasibility study

P Nadkarni et al. J Am Med Inform Assoc. 2001 Jan-Feb.

Abstract

Objectives: To explore the feasibility of using the National Library of Medicine's Unified Medical Language System (UMLS) Metathesaurus as the basis for a computational strategy to identify concepts in medical narrative text preparatory to indexing. To quantitatively evaluate this strategy in terms of true positives, false positives (spuriously identified concepts) and false negatives (concepts missed by the identification process).

Methods: Using the 1999 UMLS Metathesaurus, the authors processed a training set of 100 documents (50 discharge summaries, 50 surgical notes) with a concept-identification program, whose output was manually analyzed. They flagged concepts that were erroneously identified and added new concepts that were not identified by the program, recording the reason for failure in such cases. After several refinements to both their algorithm and the UMLS subset on which it operated, they deployed the program on a test set of 24 documents (12 of each kind).

Results: Of 8,745 matches in the training set, 7,227 (82.6 percent ) were true positives, whereas of 1,701 matches in the test set, 1, 298 (76.3 percent) were true positives. Matches other than true positive indicated potential problems in production-mode concept indexing. Examples of causes of problems were redundant concepts in the UMLS, homonyms, acronyms, abbreviations and elisions, concepts that were missing from the UMLS, proper names, and spelling errors.

Conclusions: The error rate was too high for concept indexing to be the only production-mode means of preprocessing medical narrative. Considerable curation needs to be performed to define a UMLS subset that is suitable for concept matching.

PubMed Disclaimer

Comment in

References

    1. Salton G. Automatic text processing: the transformation, analysis, and retrieval of information by computer. Reading, Mass: Addison-Wesley, 1989.
    1. Hersh WR. Information Retrieval: A Health Care Perspective. New York: Springer-Verlag, 1996.
    1. Lindberg DAB, Humphreys BL, McCray AT. The Unified Medical Language System. Methods Inf Med. 1993;32:281–91. - PMC - PubMed
    1. Salton G, Wu H, Yu CT. Measurement of Term Importance in Automatic Indexing. J Am Soc Inf Sci. 1981;32(3):175–86.
    1. Wilbur WJ, Yang Y. An analysis of statistical term strength and its use in the indexing and retrieval of molecular biology texts. Comput Biol Med. 1996;26(3):209–22. - PubMed

Publication types