EVALUATION OF A LEXICALLY ASSIGN, LOGICALLY REFINE STRATEGY FOR SEMIAUTOMATED INTEGRATION OF OVERLAPPING TERMINOLOGIES

Objective: To evaluate a ''lexically assign, logically refine'' (LALR) strategy for merging overlapping healthcare terminologies. This strat egy combines description logic classification with lexical techniques that propose initial term definitions. The lexically suggested initial definitions are manually refined by domain experts to yield descripti on logic definitions for each term in the overlapping terminologies of interest. Logic-based techniques are then used to merge defined terms . Methods: A LALR strategy was applied to 7,763 LOINC and 2,050 SNOMED procedure terms using a common set of defining relationships taken fr om the LOINC data model. Candidate value restrictions were derived by lexically comparing the procedure's name with other terms contained in the reference SNOMED topography, living organism, function, and chemi cal axes. These candidate restrictions were reviewed by a domain exper t, transformed into terminologic definitions for each of the terms, an d then algorithmically classified. Results: The authors successfully d efined 5,724 (73%) LOINC and 1,151 (56%) SNOMED procedure terms using a LALR strategy. Algorithmic classification of the defined concepts re sulted in an organization mirroring that of the reference hierarchies. The classification techniques appropriately placed more detailed LOIN C terms underneath the corresponding SNOMED terms, thus forming a comp lementary relationship between the LOINC and SNOMED terms. Discussion: LALR is a successful strategy for merging overlapping terminologies i n a test case where both terminologies can be defined using the same d efining relationships, and where value restrictions can be drawn from a single reference hierarchy. Those concepts not having lexically sugg ested value restrictions frequently indicate gaps in the reference hie rarchy.