Analyzing SNOMED CT's Historical Data: Pitfalls and Possibilities.
Werner Ceusters1, Jonathan P Bona1
1Department of Biomedical Informatics, University at Buffalo, Buffalo, NY.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|March 9, 2017
Summary
This study introduces a method to analyze changes in SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) using Release Format 2. It uncovers implicit assumptions within SNOMED CT
Area of Science:
- Medical Informatics
- Computational Linguistics
- Knowledge Representation
Background:
- SNOMED CT Release Format 2 (RF2) offers improved versioning and structural descriptions over previous formats.
- Understanding changes in SNOMED CT components across versions is crucial for its effective use.
Observation:
- RF2 partially formalizes changes related to associative relations, inactivation reasons, and semantic tag modifications in fully specified names.
- Specific patterns of change in semantic tags and their relation to inactivations are not fully captured by RF2.
Findings:
- A novel data conversion methodology is proposed, creating 'history profiles' from SNOMED CT component assertions.
- Formal Concept Analysis is applied to these profiles to identify valid implications and implicit assumptions.
Implications:
- This approach can reveal hidden assumptions within SNOMED CT's structure, enhancing its interpretability.
- The methodology aids in understanding the evolution of clinical terminology and its underlying logic.
