A comparison of Intelligent Mapper and document similarity scores for mapping local radiology terms to LOINC.

Daniel J. Vreeman, Clement J. McDonald

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

We developed a program for mapping local radiology system terms to LOINC that returns a ranked list of candidate LOINC codes based on document similarity scores. We compared the performance of this program with the Intelligent Mapper (IM) program in mapping diagnostic radiology terms to LOINC. The cosine similarity score ranked the correct LOINC code first in 34% of the terms in our development set and 39% of the terms from our test set, compared with IM's ranking of the correct LOINC code first in 83% of the terms in our development set and 92% of the terms in our test set. This study demonstrates the challenges in using document similarity scores for mapping to LOINC. Because vocabulary mapping is a resource-intensive step in integrating data from disparate systems, we need continued refinement of automated tools to help reduce the effort required.

Original languageEnglish (US)
Pages (from-to)809-813
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - 2006

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Logical Observation Identifiers Names and Codes
Radiology
Vocabulary
Information Systems

ASJC Scopus subject areas

  • Medicine(all)

Cite this

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abstract = "We developed a program for mapping local radiology system terms to LOINC that returns a ranked list of candidate LOINC codes based on document similarity scores. We compared the performance of this program with the Intelligent Mapper (IM) program in mapping diagnostic radiology terms to LOINC. The cosine similarity score ranked the correct LOINC code first in 34{\%} of the terms in our development set and 39{\%} of the terms from our test set, compared with IM's ranking of the correct LOINC code first in 83{\%} of the terms in our development set and 92{\%} of the terms in our test set. This study demonstrates the challenges in using document similarity scores for mapping to LOINC. Because vocabulary mapping is a resource-intensive step in integrating data from disparate systems, we need continued refinement of automated tools to help reduce the effort required.",
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