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|August 23, 2024
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Summary
This summary is machine-generated.

This study explores converting Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR) data into graph format for clinical research. A generalized extract-transform-load (ETL) process was developed for automated knowledge graph creation.

Keywords:
Clinical research dataETLFHIRMeDaXknowledge graph

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Area of Science:

  • Medical Informatics
  • Data Science
  • Bioinformatics

Background:

  • Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR) is the standard for clinical data exchange in Germany.
  • Graph databases (GDBs) offer advantages for integrating complex and heterogeneous medical data.
  • Analyzing raw FHIR data directly for research can be challenging.

Purpose of the Study:

  • To evaluate different extract-transform-load (ETL) approaches for converting FHIR data into a graph format.
  • To develop a generalized ETL process for creating knowledge graphs (KGs) from clinical research data.
  • To implement a prototype pipeline for automated KG creation and ontological structuring.

Main Methods:

  • Tested various ETL methods for FHIR to graph conversion.
  • Designed a generalized ETL process for knowledge graph generation.
  • Implemented a prototypic pipeline for automated KG creation and ontological structuring using synthetic patient data.

Main Results:

  • A generalized ETL process for converting FHIR data to graph format was designed and implemented.
  • A prototype knowledge graph (MeDaX-KG) was successfully created from synthetic patient data.
  • The developed approach demonstrates ease of customization for additional data types and formats.

Conclusions:

  • The developed ETL approach facilitates the conversion of FHIR data into knowledge graphs for enhanced clinical research.
  • The MeDaX-KG prototype showcases a viable method for automated knowledge graph creation and structuring.
  • The approach is adaptable and can be extended to incorporate diverse data types and formats.