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BGLM: big data-guided LOINC mapping with multi-language support.

Ke Liu1,2, Martin Witteveen-Lane3, Benjamin S Glicksberg4,5

  • 1Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.

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Summary
This summary is machine-generated.

A new tool, big data-guided LOINC code mapper (BGLM), maps lab test codes to standardized LOINC terminology using patient data. This method offers robust multi-language support for harmonizing electronic health records.

Keywords:
LOINC code mappingbig dataelectronic health recordsmulti-language support

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Area of Science:

  • Biomedical Informatics
  • Health Data Standardization

Background:

  • Harmonizing electronic health record (EHR) data across institutions requires mapping local lab test codes to standardized Logical Observation Identifiers Names and Codes (LOINC) terminology.
  • Existing LOINC code mappers often rely on text-mining and lack robust multi-language support.

Purpose of the Study:

  • To introduce a novel, language-independent tool for mapping internal lab test codes to LOINC terminology.
  • To leverage big data from EHR systems for improved LOINC coding.

Main Methods:

  • Developed the big data-guided LOINC code mapper (BGLM) tool.
  • Employed distributional similarity of patient data for mapping lab test codes.
  • Validated BGLM performance using real-world EHR datasets and controlled false discovery rates.

Main Results:

  • Achieved high mapping precision with BGLM under proper false discovery rate control.
  • Demonstrated that BGLM mapping results can enhance the performance of existing tools like Regenstrief LOINC Mapping Assistant (RELMA).

Conclusions:

  • BGLM offers a new, language-agnostic approach for LOINC code mapping in EHR systems.
  • The tool is freely available, promoting wider adoption and data harmonization.