FHIR-Ontop-OMOP: Building clinical knowledge graphs in FHIR RDF with the OMOP Common data Model.

Journal: Journal of biomedical informatics
PMID: 36089199

Abstract

BACKGROUND: Knowledge graphs (KGs) play a key role to enable explainable artificial intelligence (AI) applications in healthcare. Constructing clinical knowledge graphs (CKGs) against heterogeneous electronic health records (EHRs) has been desired by the research and healthcare AI communities. From the standardization perspective, community-based standards such as the Fast Healthcare Interoperability Resources (FHIR) and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) are increasingly used to represent and standardize EHR data for clinical data analytics, however, the potential of such a standard on building CKG has not been well investigated.

Authors

  • Guohui Xiao
    University of Bergen, Norway; University of Oslo, Norway; Ontopic S.r.l., Italy. Electronic address: guohui.xiao@uib.no.
  • Emily Pfaff
    University of North Carolina, Chapel Hill, NC, USA.
  • Eric Prud'hommeaux
    Janeiro Digital, Boston, MA, USA.
  • David Booth
    Yosemite Project, Somerville, MA, USA.
  • Deepak K Sharma
    Mayo Clinic, Rochester, MN, USA.
  • Nan Huo
    Mayo Clinic, Rochester, MN, USA.
  • Yue Yu
    Department of Mathematics, Lehigh University, Bethlehem, PA, USA.
  • Nansu Zong
    Health System Department of Biomedical Informatics, University of California, San Diego, La Jolla, California, USA.
  • Kathryn J Ruddy
    Mayo Clinic, Rochester, MN, USA.
  • Christopher G Chute
  • Guoqian Jiang
    Mayo Clinic College of Medicine, Rochester, MN, USA.

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