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. 2018:255:20-24.

Terminology Coverage from Semantic Annotated Health Documents

Marie Ndangang  1 , Julien Grosjean  1 , Romain Lelong  1 , Badisse Dahamna  1 , Ivan Kergourlay  1 , Nicolas Griffon  1 , Stéfan J Darmoni  1
Affiliations
  • PMID: 30306899

Terminology Coverage from Semantic Annotated Health Documents

Marie Ndangang et al. Stud Health Technol Inform. 2018.

Abstract

Background: Unstructured health documents (e.g. discharge summaries) represent an important and unavoidable source of information.

Methods: A semantic annotator identified all the concepts present in the health documents from the clinical data warehouse of the Rouen University Hospital.

Results: 2,087,784,055 annotations were generated from a corpus of about 11.9 million documents with an average of 175 annotations per document. SNOMED CT, NCIt and MeSH were the top 3 terminologies that reported the most annotation.

Discussion: As expected, the most general terminologies with the most translated concepts were those with the most concepts identified.

Keywords: Big data analytics; Semantic Data Warehouse; Semantic annotator; Terminology server.

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