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[Terminologies or classifications. What does the future hold?].

J Ingenerf1

  • 1Institut für Medizinische Informatik, Universität zu Lübeck, Lübeck, Germany. ingenerf@imi.uni-luebeck.de

Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz
|August 7, 2007
PubMed
Summary
This summary is machine-generated.

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International journal of medical informatics·2001

Standard classifications like ICD-10 have limitations. Terminologies such as SNOMED CT offer a compositional approach for machine-readable patient data, enhancing decision support.

Area of Science:

  • Medical Informatics
  • Health Terminology
  • Knowledge Representation

Context:

  • Current medical classifications (e.g., ICD-10) rely on hierarchical, mutually exclusive structures, often necessitating residual categories.
  • A machine-manipulable level of concepts is crucial for knowledge-based decision support and semantic integration of patient data.

Purpose:

  • To highlight the need for advanced terminologies beyond traditional classifications.
  • To introduce compositional terminologies as a solution for managing complex medical concepts.

Summary:

  • Compositional terminologies, exemplified by SNOMED CT, utilize logical approaches akin to linguistic "LEGO principles" to construct complex concepts from primitive building blocks.
  • This approach enables sophisticated machine interpretation of patient data, moving beyond the limitations of enumerative terminologies like LOINC for large domains.

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Impact:

  • Facilitates semantic integration of diverse patient data.
  • Enables advanced machine-supported interpretation of clinical information.
  • Improves the efficacy of decision support modules, particularly in areas like drug therapy.