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Automated LOINC Mapping of Persian-English Mixed-Language Clinical Laboratory Test Names.

Abbas Sheikh Taheri1, Ehsan Bitaraf2, Maani Beigy3

  • 1Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.

Studies in Health Technology and Informatics
|May 12, 2026
PubMed
Summary

This study introduces an automated service for mapping mixed Persian-English clinical lab test names to LOINC codes. The system achieved 78% accuracy, improving multilingual data interoperability in healthcare.

Keywords:
Artificial intelligenceLOINCLaboratoryMappingN-GramNatural language processingText mining

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Area of Science:

  • Medical Informatics
  • Clinical Laboratory Science
  • Natural Language Processing

Background:

  • Clinical laboratory test names in Iran often use non-standardized mixed Persian-English formats.
  • Lack of standardized terminology hinders data interoperability in healthcare systems.

Purpose of the Study:

  • To develop an intelligent service for automated mapping of mixed-language clinical laboratory test names to LOINC (Logical Observation Identifiers Names and Codes).
  • To address the challenge of multilingual laboratory data interoperability.

Main Methods:

  • A dataset of 1,400 local laboratory test names and codes was curated.
  • Manual mapping to LOINC by two experts established a gold standard.
  • An N-Gram-based similarity approach with adjustable thresholds was developed for automated mapping.

Main Results:

  • The automated system achieved approximately 78% correct mapping at similarity thresholds between 0.7 and 0.8.
  • At a threshold of 0.8, the system reached an agreement of 0.84 with expert coders, showing 0.86 precision and sensitivity.
  • Performance demonstrated a trade-off between coverage and accuracy based on similarity thresholds.

Conclusions:

  • Automated LOINC mapping is feasible for mixed Persian-English clinical laboratory data.
  • The developed service effectively addresses a significant gap in multilingual laboratory data interoperability.
  • This approach enhances the integration of real-world health information systems.