Tae Youn Kim1. 1. a Betty Irene Moore School Nursing, University of California Davis , Sacramento , CA , USA.
Abstract
OBJECTIVES: The purpose of this study was to examine the feasibility of automating lexical cross-mapping of a logic-based nursing terminology (ICNP) to SNOMED CT using the Unified Medical Language System (UMLS) maintained by the U.S. National Library of Medicine. METHODS: A two-stage approach included patterns identification, and application and evaluation of an automated term matching procedure. The performance of the automated procedure was evaluated using a test set against a gold standard (i.e. concept equivalency table) created independently by terminology experts. RESULTS: There were lexical similarities between ICNP diagnostic concepts and SNOMED CT. The automated term matching procedure was reliable as presented in recall of 65%, precision of 79%, accuracy of 82%, F-measure of 0.71 and the area under the receiver operating characteristics (ROC) curve of 0.78 (95% CI 0.73-0.83). When the automated procedure was not able to retrieve lexically matched concepts, it was also unlikely for terminology experts to identify a matched SNOMED CT concept. CONCLUSIONS: Although further research is warranted to enhance the automated matching procedure, the combination of cross-maps from UMLS and the automated procedure is useful to generate candidate mappings and thus, assist ongoing maintenance of mappings which is a significant burden to terminology developers.
OBJECTIVES: The purpose of this study was to examine the feasibility of automating lexical cross-mapping of a logic-based nursing terminology (ICNP) to SNOMED CT using the Unified Medical Language System (UMLS) maintained by the U.S. National Library of Medicine. METHODS: A two-stage approach included patterns identification, and application and evaluation of an automated term matching procedure. The performance of the automated procedure was evaluated using a test set against a gold standard (i.e. concept equivalency table) created independently by terminology experts. RESULTS: There were lexical similarities between ICNP diagnostic concepts and SNOMED CT. The automated term matching procedure was reliable as presented in recall of 65%, precision of 79%, accuracy of 82%, F-measure of 0.71 and the area under the receiver operating characteristics (ROC) curve of 0.78 (95% CI 0.73-0.83). When the automated procedure was not able to retrieve lexically matched concepts, it was also unlikely for terminology experts to identify a matched SNOMED CT concept. CONCLUSIONS: Although further research is warranted to enhance the automated matching procedure, the combination of cross-maps from UMLS and the automated procedure is useful to generate candidate mappings and thus, assist ongoing maintenance of mappings which is a significant burden to terminology developers.
Authors: Daniel J Vreeman; Swapna Abhyankar; Kenneth C Wang; Christopher Carr; Beverly Collins; Daniel L Rubin; Curtis P Langlotz Journal: J Am Med Inform Assoc Date: 2018-07-01 Impact factor: 4.497