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

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.

Keywords: LOINC; alignment process; interface terminology.

Figure 1.

The description model of LOINC concepts. The model contains six mandatory attributes (rectangles with rounded corners): four optional attributes (ovals) to refine the description of three mandatory attributes (component, system and time) and a class (rhombus).

Figure 2.

Mapping of TLAB and LOINC tokens. Tokens of TLAB and LOINC are words, excluding stop-words, which are found using a tokenization process applied to their labels based on white-spaces and punctuation.

Figure 3.

Anchoring of TLAB entities to: (a) LOINC attributes, and (b) LOINC concepts.

Figure 4.

Instance-based filtering: instantiation of laboratory results using mapped LOINC concepts. (1) Mapping of units of measure in the data warehouse to UCUM codes, (2) mapping of UCUM codes’ properties to LOINC properties, (3) validation of the mappings between anchored concepts of LOINC and TLAB that share the same LOINC property. For example, syn-ana-c1ch2-cholesterol dans le liquide d’ascite (ie, cholesterol in ascites fluid) is used in the data warehouse with “mmol/L” as a unit of measure. Consequently, only the LOINC concept, 54371-0-cholesterol (moles/volume) in peritoneal fluid, which shares the appropriate LOINC property with this unit of measure, can be used to instantiate the results.