Structural group auditing of a UMLS semantic type's extent

  • Authors:

  • Yan Chen;Huanying (Helen) Gu;Yehoshua Perl;James Geller;Michael Halper

  • Affiliations:

  • New Jersey Institute of Technology, Newark, NJ 07102, USA and Borough of Manhattan Community College, CUNY, New York, NY 10007, USA;University of Medicine and Dentistry of New Jersey, Newark, NJ 07107, USA;New Jersey Institute of Technology, Newark, NJ 07102, USA;New Jersey Institute of Technology, Newark, NJ 07102, USA;Kean University, Union, NJ 07083, USA

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2009

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Abstract

Each UMLS concept is assigned one or more of the semantic types (STs) from the Semantic Network. Due to the size and complexity of the UMLS, errors are unavoidable. We present two auditing methodologies for groups of semantically similar concepts. The straightforward procedure starts with the extent of an ST, which is the group of all concepts assigned this ST. We divide the extent into groups of concepts that have been assigned exactly the same set of STs. An algorithm finds subgroups of suspicious concepts. The human auditor is presented with these subgroups, which purportedly exhibit the same semantics, and thus she will notice different concepts with wrong or missing ST assignments. The dynamic procedure detects concepts which become suspicious in the course of the auditing process. Both procedures are applied to two semantic types. The results are compared with a comprehensive manual audit and show a very high error recall with a much higher precision.