Application of a Regenstrief RELMA V.6.6 to Map Russian Laboratory Terms to LOINC

Kopanitsa, Georgy

doi:10.3414/me15-01-0068

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…The use of the Regenstrief LOINC Mapping Assistant (RELMA) , 15 , 24 , 26 that is an open access mapping tool provided by the Regenstrief Institute for the mapping of local terms (ie, terms available in interface terminologies or in corpora of documents) to LOINC concepts. 16 RELMA uses a morphosyntactic strategy with a manual correction of mappings, thus needing users’ intervention.…”

Section: Background and Significancementioning

confidence: 99%

Aligning an interface terminology to the Logical Observation Identifiers Names and Codes (LOINC®)

et al. 2021

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Objective Our study consists in aligning the interface terminology of the Bordeaux university hospital (TLAB) to the Logical Observation Identifiers Names and Codes (LOINC). The objective was to facilitate the shared and integrated use of biological results with other health information systems. Materials and Methods We used an innovative approach based on a decomposition and re-composition of LOINC concepts according to the transversal relations that may be described between LOINC concepts and their definitional attributes. TLAB entities were first anchored to LOINC attributes and then aligned to LOINC concepts through the appropriate combination of definitional attributes. Finally, using laboratory results of the Bordeaux data-warehouse, an instance-based filtering process has been applied. Results We found a small overlap between the tokens constituting the labels of TLAB and LOINC. However, the TLAB entities have been easily aligned to LOINC attributes. Thus, 99.8% of TLAB entities have been related to a LOINC analyte and 61.0% to a LOINC system. A total of 55.4% of used TLAB entities in the hospital data-warehouse have been mapped to LOINC concepts. We performed a manual evaluation of all 1-1 mappings between TLAB entities and LOINC concepts and obtained a precision of 0.59. Conclusion We aligned TLAB and LOINC with reasonable performances, given the poor quality of TLAB labels. In terms of interoperability, the alignment of interface terminologies with LOINC could be improved through a more formal LOINC structure. This would allow queries on LOINC attributes rather than on LOINC concepts only.

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Section: Background and Significancementioning

confidence: 99%

Aligning an interface terminology to the Logical Observation Identifiers Names and Codes (LOINC®)

et al. 2021

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“…Although laboratory technologists and physicians always have a good knowledge of laboratory tests, they may lack the knowledge of how to map local laboratory test codes to LOINC codes [ 4 , 29 , 30 , 31 ]. Moreover, local test coding has always lacked information to successfully map all laboratory tests to standard terminology [ 32 ]. Previous studies developed various algorithms to automatically map local codes to LOINC codes with great efficiency and minimum human effort [ 33 , 34 , 35 ].…”

Section: Discussionmentioning

confidence: 99%

Logical Observation Identifiers Names and Codes (LOINC®) Applied to Microbiology: A National Laboratory Mapping Experience in Taiwan

et al. 2021

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Background and Objective: Logical Observation Identifiers Names and Codes (LOINC) is a universal standard for identifying laboratory tests and clinical observations. It facilitates a smooth information exchange between hospitals, locally and internationally. Although it offers immense benefits for patient care, LOINC coding is complex, resource-intensive, and requires substantial domain expertise. Our objective was to provide training and evaluate the performance of LOINC mapping of 20 pathogens from 53 hospitals participating in the National Notifiable Disease Surveillance System (NNDSS). Methods: Complete mapping codes for 20 pathogens (nine bacteria and 11 viruses) were requested from all participating hospitals to review between January 2014 and December 2016. Participating hospitals mapped those pathogens to LOINC terminology, utilizing the Regenstrief LOINC mapping assistant (RELMA) and reported to the NNDSS, beginning in January 2014. The mapping problems were identified by expert panels that classified frequently asked questionnaires (FAQs) into seven LOINC categories. Finally, proper and meaningful suggestions were provided based on the error pattern in the FAQs. A general meeting was organized if the error pattern proved to be difficult to resolve. If the experts did not conclude the local issue’s error pattern, a request was sent to the LOINC committee for resolution. Results: A total of 53 hospitals participated in our study. Of these, 26 (49.05%) used homegrown and 27 (50.95%) used outsourced LOINC mapping. Hospitals who participated in 2015 had a greater improvement in LOINC mapping than those of 2016 (26.5% vs. 3.9%). Most FAQs were related to notification principles (47%), LOINC system (42%), and LOINC property (26%) in 2014, 2015, and 2016, respectively. Conclusions: The findings of our study show that multiple stage approaches improved LOINC mapping by up to 26.5%.

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“…This situation therefore leads to a growing need for a direct mapping between local terminologies and the standardised LOINC terminology [14,15]. Semi-automatic approaches and tools, including the one distributed by Regenstrief, are proposed to assist domain experts [9], but the mapping of local terminologies with LOINC remains time-consuming and requires a lot of human expertise [8]. With the exponential advances in automatic language processing driven by increasingly efficient artificial intelligence algorithms, our study aims at proposing an innovative solution to the problem of mapping these local terminologies with the LOINC encoding.…”

Section: Background and Significancementioning

confidence: 99%

Ensuring Interoperability of Laboratory Tests and Results: A Linguistic Approach for Mapping French Laboratory Terminologies with LOINC

Patel¹,

Abel²,

Brun³

et al. 2021

Computer Science Protecting Human Society Against Epidemics

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With the increasing use of electronic patient records, interoperability of patient-related data is of primary concern and international standards have been developed to assure compatibility between health data management systems. The present study is a first step in this direction for laboratory tests written in french, following the French government's impetus for providing national health records for patients. To address the linguistic complexities inherent to this task, we adopt a natural language processing (NLP) methodology. Our pilot case study shows that computational linguistic assistance in aligning terminologies makes it 3 times faster for domain experts. The significant difference in performance between the existing state of the art and our tool reflects the impact of addressing the linguistic challenges involved in the mapping process, especially in the multilingual context.

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Application of a Regenstrief RELMA V.6.6 to Map Russian Laboratory Terms to LOINC

Cited by 9 publications

References 19 publications

Aligning an interface terminology to the Logical Observation Identifiers Names and Codes (LOINC®)

Aligning an interface terminology to the Logical Observation Identifiers Names and Codes (LOINC®)

Logical Observation Identifiers Names and Codes (LOINC®) Applied to Microbiology: A National Laboratory Mapping Experience in Taiwan

Ensuring Interoperability of Laboratory Tests and Results: A Linguistic Approach for Mapping French Laboratory Terminologies with LOINC

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