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Extending import detection algorithms for concept import from two to three biomedical terminologies.

Vipina K Keloth1, James Geller2, Yan Chen3

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Summary

Using two source terminologies algorithmically identifies more missing concepts for biomedical terminologies than single sources. This "fire ladder" pattern improves concept discovery for hierarchical structures.

Keywords:
Concept importDensity differencesNational cancer institute thesaurusSNOMED CTTerminologiesUMLS

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Area of Science:

  • Biomedical Informatics
  • Computational Linguistics
  • Knowledge Representation

Background:

  • Enriching terminologies is crucial for improving their utility.
  • Algorithmic discovery of missing concepts, particularly in IS-A hierarchies, offers a promising approach.
  • Previous methods relied on comparing a single source terminology to a target terminology.

Purpose of the Study:

  • To extend algorithmic concept discovery by using two source terminologies in tandem with a target terminology.
  • To identify specific terminology triples (UMLS) that are effective for this enhanced discovery process.
  • To evaluate the reliability and efficiency of discovering candidate concepts for import.

Main Methods:

  • Developed a method to compare two source terminologies against one target terminology.
  • Investigated the use of "fire ladder" patterns, a specific configuration of overlapping concepts.
  • Quantitatively analyzed discovered patterns and reported inter-rater agreement on concept import decisions.

Main Results:

  • Identified 55 instances of the "fire ladder" pattern across UMLS terminology triples.
  • Two domain experts agreed on importing 39 of the candidate concepts.
  • A total of 48 concepts were approved for import by at least one expert.
  • An additional 105 candidate concepts were identified as a side-effect.

Conclusions:

  • Pairs of biomedical terminologies can be effectively chained to suggest concept imports into a target terminology.
  • The tandem use of source terminologies significantly enhances the discovery of missing concepts compared to single-source methods.