A study of semantic proximity between archetype terms based on SNOMED CT relationships

  • Authors:

  • Jose Luis Allones;David Penas;María Taboada;Diego Martinez;Serafín Tellado

  • Affiliations:

  • Department of Electronics and Computer Science, University of Santiago de Compostela, Spain;Department of Electronics and Computer Science, University of Santiago de Compostela, Spain;Department of Electronics and Computer Science, University of Santiago de Compostela, Spain;Department of Applied Physics, University of Santiago de Compostela, Spain;Department of Applied Physics, University of Santiago de Compostela, Spain

  • Venue:
  • BPM' 2012 Proceedings of the 2012 international conference on Process Support and Knowledge Representation in Health Care
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

The OpenEHR archetypes have been suggested as a standard for detailing data models of electronic healthcare records, as a means of achieving interoperability between clinical systems. But, mapping terms of these clinical data models to a terminology system, such as SNOMED CT, is a crucial step to provide the required interoperability. Through this study, we aim to understand better how archetype clinical information is semantically related using SNOMED CT relationships as a reference. For this purpose, we developed an automated approach to bind archetype terms to the SNOMED CT terminology. Our method revealed a high degree of semantic similarity between the terms modeled in the archetypes and the hierarchical and logical relationships covered by SNOMED CT. It has been detected that more than 75% of the archetype terms are semantically related to other terms of the same archetype. Taking this into account, our approach applies a combination of terminological relationships-based techniques with lexical and linguistic resources. A set of 25 clinical archetypes with 477 bound terms was used to test the method. Of these, 378 terms (79%) were linked with 96% precision, 76% recall. Our approach has proven to take advantage of the SNOMED CT relationship structure, increasing the total recall by 10%. Therefore, this work shows that it is possible to automatically map archetype terms to a standard terminology with a high precision and recall, with the help of appropriate contextual and semantic information of both models.