Dataset Information

A multi-part matching strategy for mapping LOINC with laboratory terminologies.

ABSTRACT:

Objective

To address the problem of mapping local laboratory terminologies to Logical Observation Identifiers Names and Codes (LOINC). To study different ontology matching algorithms and investigate how the probability of term combinations in LOINC helps to increase match quality and reduce manual effort.

Materials and methods

We proposed two matching strategies: full name and multi-part. The multi-part approach also considers the occurrence probability of combined concept parts. It can further recommend possible combinations of concept parts to allow more local terms to be mapped. Three real-world laboratory databases from Taiwanese hospitals were used to validate the proposed strategies with respect to different quality measures and execution run time. A comparison with the commonly used tool, Regenstrief LOINC Mapping Assistant (RELMA) Lab Auto Mapper (LAM), was also carried out.

Results

The new multi-part strategy yields the best match quality, with F-measure values between 89% and 96%. It can automatically match 70-85% of the laboratory terminologies to LOINC. The recommendation step can further propose mapping to (proposed) LOINC concepts for 9-20% of the local terminology concepts. On average, 91% of the local terminology concepts can be correctly mapped to existing or newly proposed LOINC concepts.

Conclusions

The mapping quality of the multi-part strategy is significantly better than that of LAM. It enables domain experts to perform LOINC matching with little manual work. The probability of term combinations proved to be a valuable strategy for increasing the quality of match results, providing recommendations for proposed LOINC conepts, and decreasing the run time for match processing.

SUBMITTER: Lee LH

PROVIDER: S-EPMC4147612 | biostudies-literature | 2014 Sep-Oct

REPOSITORIES: biostudies-literature

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Publications

A multi-part matching strategy for mapping LOINC with laboratory terminologies.

Lee Li-Hui LH Groß Anika A Hartung Michael M Liou Der-Ming DM Rahm Erhard E

Journal of the American Medical Informatics Association : JAMIA 20131220 5

<h4>Objective</h4>To address the problem of mapping local laboratory terminologies to Logical Observation Identifiers Names and Codes (LOINC). To study different ontology matching algorithms and investigate how the probability of term combinations in LOINC helps to increase match quality and reduce manual effort.<h4>Materials and methods</h4>We proposed two matching strategies: full name and multi-part. The multi-part approach also considers the occurrence probability of combined concept parts. ...[more]

PMID: 24363318

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Project description:BackgroundSystematic study of clinical phenotypes is important for a better understanding of the genetic basis of human diseases and more effective gene-based disease management. A key aspect in facilitating such studies requires standardized representation of the phenotype data using common data elements (CDEs) and controlled biomedical vocabularies. In this study, the authors analyzed how a limited subset of phenotypic data is amenable to common definition and standardized collection, as well as how their adoption in large-scale epidemiological and genome-wide studies can significantly facilitate cross-study analysis.MethodsThe authors mapped phenotype data dictionaries from five different eMERGE (Electronic Medical Records and Genomics) Network sites studying multiple diseases such as peripheral arterial disease and type 2 diabetes. For mapping, standardized terminological and metadata repository resources, such as the caDSR (Cancer Data Standards Registry and Repository) and SNOMED CT (Systematized Nomenclature of Medicine), were used. The mapping process comprised both lexical (via searching for relevant pre-coordinated concepts and data elements) and semantic (via post-coordination) techniques. Where feasible, new data elements were curated to enhance the coverage during mapping. A web-based application was also developed to uniformly represent and query the mapped data elements from different eMERGE studies.ResultsApproximately 60% of the target data elements (95 out of 157) could be mapped using simple lexical analysis techniques on pre-coordinated terms and concepts before any additional curation of terminology and metadata resources was initiated by eMERGE investigators. After curation of 54 new caDSR CDEs and nine new NCI thesaurus concepts and using post-coordination, the authors were able to map the remaining 40% of data elements to caDSR and SNOMED CT. A web-based tool was also implemented to assist in semi-automatic mapping of data elements.ConclusionThis study emphasizes the requirement for standardized representation of clinical research data using existing metadata and terminology resources and provides simple techniques and software for data element mapping using experiences from the eMERGE Network.

Dataset Information

A multi-part matching strategy for mapping LOINC with laboratory terminologies.

Objective

Materials and methods

Results

Conclusions

Publications

A multi-part matching strategy for mapping LOINC with laboratory terminologies.

Similar Datasets

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