首页|Clustering clinical models from local electronic health records based on semantic similarity
Clustering clinical models from local electronic health records based on semantic similarity
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NSTL
Elsevier
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.
Computerized medical recordsSemanticsSNOMED CTMedical record linkage/standardsMedical record linkage/methodsAlgorithms
NSTL
Elsevier
