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A review of auditing techniques for the Unified Medical Language System.
Zheng, Ling; He, Zhe; Wei, Duo; Keloth, Vipina; Fan, Jung-Wei; Lindemann, Luke; Zhu, Xinxin; Cimino, James J; Perl, Yehoshua.
Affiliation
  • Zheng L; Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, New Jersey, USA.
  • He Z; School of Information, Florida State University, Tallahassee, Florida, USA.
  • Wei D; School of Business, Stockton University, Galloway, New Jersey, USA.
  • Keloth V; Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA.
  • Fan JW; Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
  • Lindemann L; Center for Biomedical Data Science, Yale School of Medicine, New Haven, Connecticut, USA.
  • Zhu X; Center for Biomedical Data Science, Yale School of Medicine, New Haven, Connecticut, USA.
  • Cimino JJ; Informatics Institute, University of Alabama at Birmingham, Birmingham, Alabama, USA.
  • Perl Y; Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA.
J Am Med Inform Assoc ; 27(10): 1625-1638, 2020 10 01.
Article in En | MEDLINE | ID: mdl-32766692
ABSTRACT

OBJECTIVE:

The study sought to describe the literature related to the development of methods for auditing the Unified Medical Language System (UMLS), with particular attention to identifying errors and inconsistencies of attributes of the concepts in the UMLS Metathesaurus. MATERIALS AND

METHODS:

We applied the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach by searching the MEDLINE database and Google Scholar for studies referencing the UMLS and any of several terms related to auditing, error detection, and quality assurance. A qualitative analysis and summarization of articles that met inclusion criteria were performed.

RESULTS:

Eighty-three studies were reviewed in detail. We first categorized techniques based on various aspects including concepts, concept names, and synonymy (n = 37), semantic type assignments (n = 36), hierarchical relationships (n = 24), lateral relationships (n = 12), ontology enrichment (n = 8), and ontology alignment (n = 18). We also categorized the methods according to their level of automation (ie, automated systematic, automated heuristic, or manual) and the type of knowledge used (ie, intrinsic or extrinsic knowledge).

CONCLUSIONS:

This study is a comprehensive review of the published methods for auditing the various conceptual aspects of the UMLS. Categorizing the auditing techniques according to the various aspects will enable the curators of the UMLS as well as researchers comprehensive easy access to this wealth of knowledge (eg, for auditing lateral relationships in the UMLS). We also reviewed ontology enrichment and alignment techniques due to their critical use of and impact on the UMLS.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Quality Control / Unified Medical Language System Type of study: Prognostic_studies / Qualitative_research Language: En Journal: J Am Med Inform Assoc Journal subject: INFORMATICA MEDICA Year: 2020 Document type: Article Affiliation country: Estados Unidos

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Quality Control / Unified Medical Language System Type of study: Prognostic_studies / Qualitative_research Language: En Journal: J Am Med Inform Assoc Journal subject: INFORMATICA MEDICA Year: 2020 Document type: Article Affiliation country: Estados Unidos