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Assisting Data Retrieval with a Drug Knowledge Graph.

Romain Lelong1,2, Badisse Dahamna1,2, Romain Leguillon1

  • 1CHU Rouen, Department of Biomedical Informatics, F-76000 Rouen, France.

Studies in Health Technology and Informatics
|January 22, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a knowledge graph-based information retrieval system for medication orders at the University Hospital of Rouen. The system aids health professionals in drug selection, showing promising initial results for prescription searches.

Keywords:
Drug Information RetrievalKnowledge GraphSemantic Network

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Area of Science:

  • Health Informatics
  • Medical Knowledge Management
  • Clinical Decision Support

Background:

  • The Normandy health data warehouse (EDSaN) consolidates medication orders from the University Hospital of Rouen.
  • Effective retrieval of medication information is crucial for clinical decision-making and patient safety.
  • Existing systems may lack the semantic richness to support complex drug selection processes.

Purpose of the Study:

  • To design and evaluate an information retrieval system for medication orders within the EDSaN data warehouse.
  • To leverage a semantically augmented knowledge graph to enhance drug prescription searches.
  • To assist health professionals in the selection of appropriate medications.

Main Methods:

  • Development of a complex, semantically augmented knowledge graph for EDSaN drug prescriptions.
  • Implementation of an information retrieval system utilizing this knowledge graph.
  • Manual evaluation of the relevance of retrieved drugs by domain experts.

Main Results:

  • The designed information retrieval system demonstrated encouraging relevance in manual evaluations.
  • The knowledge graph effectively supports the semantic understanding of medication orders.
  • Initial results indicate the system's potential to aid health professionals in drug selection.

Conclusions:

  • The developed knowledge graph-based system shows promise for improving medication order retrieval.
  • Further analysis and refinement of the ranking algorithm are necessary for enhanced performance.
  • Future work will focus on optimizing the retrieval and ranking methods for clinical application.