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

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Abstract 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.
Title: Aligning an interface terminology to the Logical Observation Identifiers Names and Codes (LOINC®)
Description:
Abstract 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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