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

Author:

Liu Ke12ORCID,Witteveen-Lane Martin3,Glicksberg Benjamin S45ORCID,Kulkarni Omkar3,Shankar Rama2,Chekalin Evgeny2,Paithankar Shreya2,Yang Jeanne6,Chesla Dave37,Chen Bin28

Affiliation:

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

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.

Funder

Spectrum Health-MSU Alliance Fund

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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