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Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC

Bader Aldughayfiq1, Farzeen Ashfaq2, N Z Jhanjhi2

  • 1Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.

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Summary

Knowledge graphs effectively capture semantic relationships within electronic health records (EHRs), improving data analysis efficiency and accuracy for better patient outcomes and risk factor identification.

Keywords:
GraphDBMIMIC IIIdata analysiselectronic health recordsknowledge graphsontologysemantic relationships

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Area of Science:

  • Health Informatics
  • Data Science
  • Knowledge Representation

Background:

  • Electronic health records (EHRs) are vital but often fragmented and difficult to analyze.
  • Knowledge graphs offer a powerful method for representing complex data relationships.

Purpose of the Study:

  • To investigate if a knowledge graph built from the MIMIC III dataset using GraphDB can capture semantic relationships in EHRs.
  • To determine if this approach enhances data analysis efficiency and accuracy.

Main Methods:

  • Mapped the MIMIC III dataset to an ontology using text refinement and Protege.
  • Constructed a knowledge graph with GraphDB.
  • Utilized SPARQL queries for data retrieval and analysis.

Main Results:

  • Knowledge graphs effectively capture semantic relationships within EHR data.
  • The approach enables more efficient and accurate analysis of EHRs.
  • Implementation provides insights into patient outcomes and risk factors.

Conclusions:

  • Knowledge graphs are effective tools for EHR data analysis, improving decision-making.
  • This research supports the use of knowledge graphs in healthcare for holistic data analysis.
  • Findings lay the groundwork for future research in healthcare knowledge graphs.