title: Clustering clinical models from local electronic health records based on semantic similarity.
abstract: BACKGROUND
  Clinical models in electronic health records are typically expressed as templates which support the multiple clinical workflows in which the system is used. The templates are often designed using local rather than standard information models and terminology, which hinders semantic interoperability. Semantic challenges can be solved by harmonizing and standardizing clinical models. However, methods supporting harmonization based on existing clinical models are lacking. One approach is to explore semantic similarity estimation as a basis of an analytical framework. Therefore, the aim of this study is to develop and apply methods for intrinsic similarity-estimation based analysis that can compare and give an overview of multiple clinical models.
  
  
  METHODS
  For a similarity estimate to be intrinsic it should be based on an established ontology, for which SNOMED CT was chosen. In this study, Lin similarity estimates and Sokal and Sneath similarity estimates were used together with two aggregation techniques (average and best-match-average respectively) resulting in a total of four methods. The similarity estimations are used to hierarchically cluster templates. The test material consists of templates from Danish and Swedish EHR systems. The test material was used to evaluate how the four different methods perform.
  
  
  CONCLUSIONS
  The best-match-average aggregation technique performed better in terms of clustering similar templates than the average aggregation technique. No difference could be seen in terms of the choice of similarity estimate in this study, but the finding may be different for other datasets. The dendrograms resulting from the hierarchical clustering gave an overview of the templates and a basis of further analysis.
  
  
  CONCLUSIONS
  Hierarchical clustering of templates based on SNOMED CT and semantic similarity estimation with best-match-average aggregation technique can be used for comparison and summarization of multiple templates. Consequently, it can provide a valuable tool for harmonization and standardization of clinical models.
authors.affiliation: Aalborg University, Department of Health Science and Technology, Fredrik Bajers Vej 7D2, 9220 Aalborg, Denmark. Electronic address: kirse@hst.aau.dk.
authors.forename: Kirstine Rosenbeck
authors.initials: KR
authors.lastname: Gøeg
authors.affiliation: Academic Medical Center - University of Amsterdam, Department of Medical Informatics, P.O. Box 22700, 1100 DE Amsterdam, The Netherlands; Linköping University, Department of Biomedical Engineering, SE-581 83 Linköping, Sweden.
authors.forename: Ronald
authors.initials: R
authors.lastname: Cornet
authors.affiliation: Aalborg University, Department of Health Science and Technology, Fredrik Bajers Vej 7D2, 9220 Aalborg, Denmark.
authors.forename: Stig Kjær
authors.initials: SK
authors.lastname: Andersen
mesh_terms: D016000:Cluster Analysis; D057286:Electronic Health Records / Q000145:classification*; D006801:Humans; D008498:Medical Record Linkage; D012660:Semantics; D039061:Systematized Nomenclature of Medicine
keywords: Algorithms; Computerized medical records; Medical record linkage/methods; Medical record linkage/standards; SNOMED CT; Semantics