A Semantic Transformation Methodology for the Secondary Use of Observational Healthcare Data in Postmarketing Safety Studies

Pacaci A., Gonul S., Sinaci A. A. , Yuksel M., Erturkmen G. B. L.

FRONTIERS IN PHARMACOLOGY, vol.9, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9
  • Publication Date: 2018
  • Doi Number: 10.3389/fphar.2018.00435
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: semantic transformation, healthcare datasets, common data model, postmarketing safety study, pharmacovigilance, COMMON DATA MODEL, DRUG SAFETY, SURVEILLANCE, TRIALS, RECORDS, EVENTS
  • Middle East Technical University Affiliated: No


Background: Utilization of the available observational healthcare datasets is key to complement and strengthen the postmarketing safety studies. Use of common data models (CDM) is the predominant approach in order to enable large scale systematic analyses on disparate data models and vocabularies. Current CDM transformation practices depend on proprietarily developed Extract-Transform-Load (ETL) procedures, which require knowledge both on the semantics and technical characteristics of the source datasets and target CDM.