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Related Experiment Videos

A formal representation for messages containing compositional expressions.

Peter L Elkin1, Steven H Brown, Michael J Lincoln

  • 1Mayo Clinic, Baldwin 4B, 200 First Street Southwest, Rochester, MN 55905, USA.

International Journal of Medical Informatics
|October 2, 2003
PubMed
Summary
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Controlled vocabularies need to be comprehensive and reliable for clinical use. A new formalism for compositional expressions ensures data comparability, addressing limitations of pre-coordinated terminologies.

Area of Science:

  • Medical Informatics
  • Computational Linguistics
  • Health Terminology

Background:

  • Clinically useful controlled vocabularies must be complete and reliable.
  • Pre-coordinating all possible healthcare expressions is infeasible due to variations in practice, granularity, terminology growth, and maintenance costs.
  • Enumerated terminologies cannot be truly comprehensive.

Purpose of the Study:

  • To address the challenge of content completeness in healthcare terminologies.
  • To propose a solution for ensuring the comparability of post-coordinated expressions.
  • To present a formalism for storing and transmitting compositional expressions for algorithmic comparability.

Main Methods:

  • Developed a formalism for storing and sending messages with compositional expressions.

Related Experiment Videos

  • Utilized a large-scale reference terminology within the formalism.
  • Advocated for transmitting the upward transitive closure of concept subsumption.

    • Main Results:

    • The proposed formalism enables algorithmic determination of data comparability in messages.
    • Normalization of terminology content and semantics is crucial for comparable post-coordinated data.
    • A robust storage and messaging paradigm is required for complex compositional expressions.

    Conclusions:

    • Compositional terminologies offer a solution for content completeness but risk non-comparable expressions.
    • The presented formalism aims to ensure algorithmic comparability of data.
    • Transmitting concept subsumption improves data comparability and reduces reliance on local terminology versions.