Auditing concept categorizations in the UMLS

  • Authors:

  • Huanying (Helen) Gu;Yehoshua Perl;Gai Elhanan;Hua Min;Li Zhang;Yi Peng

  • Affiliations:

  • Department of Health Informatics, University of Medicine and Dentistry of NJ, Newark, NJ 07107, USA;CS Department, New Jersey Institute of Technology, Newark, NJ 07102, USA;Info-X Inc., Northvale, NJ 07647, USA;CS Department, New Jersey Institute of Technology, Newark, NJ 07102, USA;CS Department, New Jersey Institute of Technology, Newark, NJ 07102, USA;CS Department, New Jersey Institute of Technology, Newark, NJ 07102, USA

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2004

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Abstract

The Unified Medical Language System (UMLS) integrates about 880,000 concepts from 100 biomedical terminologies. Each concept is categorized to at least one semantic type of the Semantic Network. During the integration, it is unavoidable that some categorization errors and inconsistencies will be introduced. In this paper, we present an auditing technique to find such errors and inconsistencies. Our technique is based on an expert reviewing the pure intersections of meta-semantic types of a metaschema, a compact abstract view of the UMLS Semantic Network. We use a divide and conquer approach, handling differently small pure intersections and medium to large pure intersections. By using this approach, we limit the number of concepts reviewed, for which we expect a high percentage of errors. We reviewed all concepts in 657 pure intersections containing one to 10 concepts. Various kinds of errors are identified and the analysis of the results are presented in the paper. Also, we checked the pure intersections containing more than 10 concepts for their semantic soundness, where the semantically suspicious pure intersections are presented in the paper and their concepts are reviewed.