Semantic mappings and locality of nursing diagnostic concepts in UMLS

  • Authors:

  • Tae Youn Kim;Amy Coenen;Nicholas Hardiker

  • Affiliations:

  • College of Nursing, University of Wisconsin-Milwaukee, 1921 E. Hartford Avenue, P.O. Box 413, Milwaukee, WI 53201-0413, USA;College of Nursing, University of Wisconsin-Milwaukee, 1921 E. Hartford Avenue, P.O. Box 413, Milwaukee, WI 53201-0413, USA;School of Nursing & Midwifery, University of Salford, Mary Seacole Building, Greater Manchester M6 6PU, UK

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2012

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Abstract

One solution for enhancing the interoperability between nursing information systems, given the availability of multiple nursing terminologies, is to cross-map existing nursing concepts. The Unified Medical Language System (UMLS) developed and distributed by the National Library of Medicine (NLM) is a knowledge resource containing cross-mappings of various terminologies in a unified framework. While the knowledge resource has been available for the last two decades, little research on the representation of nursing terminologies in UMLS has been conducted. As a first step, UMLS semantic mappings and concept locality were examined for nursing diagnostic concepts or problems selected from three terminologies (i.e., CCC, ICNP, and NANDA-I) along with corresponding SNOMED CT concepts. The evaluation of UMLS semantic mappings was conducted by measuring the proportion of concordance between UMLS and human expert mappings. The semantic locality of nursing diagnostic concepts was assessed by examining the associations of select concepts and the placement of the nursing concepts on the Semantic Network and Group. The study found that the UMLS mappings of CCC and NANDA-I concepts to SNOMED CT were highly concordant to expert mappings. The level of concordance in mappings of ICNP to SNOMED CT, CCC and NANDA-I within UMLS was relatively low, indicating the need for further research and development. Likewise, the semantic locality of ICNP concepts could be further improved. Various stakeholders need to collaborate to enhance the NLM knowledge resource and the interoperability of nursing data within the discipline as well as across health-related disciplines.