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A Querying Method over RDF-ized Health Level Seven v2.5 Messages Using Life Science Knowledge Resources.

Yoshimasa Kawazoe1, Takeshi Imai, Kazuhiko Ohe

  • 1Department of Healthcare Information Management, The University of Tokyo Hospital, Tokyo, Japan. kawazoe@hcc.h.u-tokyo.ac.jp.

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Summary
This summary is machine-generated.

This study demonstrates a feasible method for querying large-scale clinical data by converting Health Level Seven version 2.5 (HL7 v2.5) messages into Resource Description Framework (RDF) format. This approach enhances clinical data usability and supports applications like adverse drug event monitoring.

Keywords:
Semantic Webelectronic health recordshealth level seveninformation storage and retrievallinked open data

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Area of Science:

  • Biomedical Informatics
  • Data Science
  • Semantic Web Technologies

Background:

  • Health Level Seven version 2.5 (HL7 v2.5) is a standard for clinical information exchange.
  • Semantic Web technologies can integrate HL7 v2.5 data with life science knowledge.

Purpose of the Study:

  • To demonstrate a feasible querying method for large-scale Resource Description Framework (RDF)-ized HL7 v2.5 messages.
  • To utilize publicly available drug databases for enhanced querying.

Main Methods:

  • Developed a method to convert HL7 v2.5 messages into RDF.
  • Converted five drug databases into RDF and linked them.
  • Created a query expansion method using drug classes and temporal patterns for clinical data search.

Main Results:

  • Converted approximately 650 million RDF triples for medication orders and 790 million for lab results.
  • Queries for adverse drug event detection were successfully represented in SPARQL.
  • Query execution times were feasible, increasing logarithmically or linearly with data volume.

Conclusions:

  • Proposed methods allow separation of knowledge resources and clinical data, improving clinical data usability.
  • HL7 v2.5 messages converted to RDF can be searched via SPARQL without structural modification.
  • This approach supports clinical intelligence applications like adverse drug event monitoring and cohort selection.