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

Ke Liu; Martin Witteveen-Lane; Benjamin S Glicksberg; Omkar Kulkarni; Rama Shankar; Evgeny Chekalin; Shreya Paithankar; Jeanne Yang; Dave Chesla; Bin Chen

doi:10.1093/jamiaopen/ooac099

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

JAMIA Open. 2022 Nov 25;5(4):ooac099. doi: 10.1093/jamiaopen/ooac099. eCollection 2022 Dec.

Authors

Ke Liu^{1

2}, Martin Witteveen-Lane³, Benjamin S Glicksberg^{4

5}, Omkar Kulkarni³, Rama Shankar², Evgeny Chekalin², Shreya Paithankar², Jeanne Yang⁶, Dave Chesla^{3

7}, Bin Chen^{2

8}

Affiliations

¹ Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
² Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA.
³ Office of Research, Spectrum Health, Grand Rapids, Michigan, USA.
⁴ Icahn School of Medicine at Mount Sinai, The Hasso Plattner Institute for Digital Health at Mount Sinai, New York City, New York, USA.
⁵ Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.
⁶ College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, Michigan, USA.
⁷ Department of Obstetrics, Gynecology and Reproductive Biology, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA.
⁸ Department of Pharmacology and Toxicology, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA.

Abstract

Motivation: Mapping internal, locally used lab test codes to standardized logical observation identifiers names and codes (LOINC) terminology has become an essential step in harmonizing electronic health record (EHR) data across different institutions. However, most existing LOINC code mappers are based on text-mining technology and do not provide robust multi-language support.

Materials and methods: We introduce a simple, yet effective tool called big data-guided LOINC code mapper (BGLM), which leverages the large amount of patient data stored in EHR systems to perform LOINC coding mapping. Distinguishing from existing methods, BGLM conducts mapping based on distributional similarity.

Results: We validated the performance of BGLM with real-world datasets and showed that high mapping precision could be achieved under proper false discovery rate control. In addition, we showed that the mapping results of BGLM could be used to boost the performance of Regenstrief LOINC Mapping Assistant (RELMA), one of the most widely used LOINC code mappers.

Conclusions: BGLM paves a new way for LOINC code mapping and therefore could be applied to EHR systems without the restriction of languages. BGLM is freely available at https://github.com/Bin-Chen-Lab/BGLM.

Keywords: LOINC code mapping; big data; electronic health records; multi-language support.