H Gu1, Y Chen, Z He, M Halper, L Chen. 1. Dr. Huanying (Helen) Gu, Computer Science Department, New York Institute of Technology, 1855 Broadway New York, NY 10023-7692, USA, E-mail: hgu03@nyit.edu.
Abstract
BACKGROUND: The Unified Medical Language System (UMLS) is one of the largest biomedical terminological systems, with over 2.5 million concepts in its Metathesaurus repository. The UMLS's Semantic Network (SN) with its collection of 133 high-level semantic types serves as an abstraction layer on top of the Metathesaurus. In particular, the SN elaborates an aspect of the Metathesaurus's concepts via the assignment of one or more types to each concept. Due to the scope and complexity of the Metathesaurus, errors are all but inevitable in this semantic-type assignment process. OBJECTIVES: To develop a semi-automated methodology to help assure the quality of semantic-type assignments within the UMLS. METHODS: The methodology uses a cross-validation strategy involving SNOMED CT's hierarchies in combination with UMLS semantic types. Semantically uniform, disjoint concept groups are generated programmatically by partitioning the collection of all concepts in the same SNOMED CT hierarchy according to their respective semantic-type assignments in the UMLS. Domain experts are then called upon to review the concepts in any group having a small number of concepts. It is our hypothesis that a semantic-type assignment combination applicable only to a very small number of concepts in a SNOMED CT hierarchy is an indicator of potential problems. RESULTS: The methodology was applied to the UMLS 2013AA release along with the SNOMED CT from January 2013. An overall error rate of 33% was found for concepts proposed by the quality-assurance methodology. Supporting our hypothesis, that number was four times higher than the error rate found in control samples. CONCLUSION: The results show that the quality-assurance methodology can aid in effective and efficient identification of UMLS semantic-type assignment errors.
BACKGROUND: The Unified Medical Language System (UMLS) is one of the largest biomedical terminological systems, with over 2.5 million concepts in its Metathesaurus repository. The UMLS's Semantic Network (SN) with its collection of 133 high-level semantic types serves as an abstraction layer on top of the Metathesaurus. In particular, the SN elaborates an aspect of the Metathesaurus's concepts via the assignment of one or more types to each concept. Due to the scope and complexity of the Metathesaurus, errors are all but inevitable in this semantic-type assignment process. OBJECTIVES: To develop a semi-automated methodology to help assure the quality of semantic-type assignments within the UMLS. METHODS: The methodology uses a cross-validation strategy involving SNOMED CT's hierarchies in combination with UMLS semantic types. Semantically uniform, disjoint concept groups are generated programmatically by partitioning the collection of all concepts in the same SNOMED CT hierarchy according to their respective semantic-type assignments in the UMLS. Domain experts are then called upon to review the concepts in any group having a small number of concepts. It is our hypothesis that a semantic-type assignment combination applicable only to a very small number of concepts in a SNOMED CT hierarchy is an indicator of potential problems. RESULTS: The methodology was applied to the UMLS 2013AA release along with the SNOMED CT from January 2013. An overall error rate of 33% was found for concepts proposed by the quality-assurance methodology. Supporting our hypothesis, that number was four times higher than the error rate found in control samples. CONCLUSION: The results show that the quality-assurance methodology can aid in effective and efficient identification of UMLS semantic-type assignment errors.
Entities:
Keywords:
Medical terminology; SNOMED CT; UMLS; UMLS auditing; auditing of terminologies; quality assurance; semantic-type assignment
Authors: Marjolein Engelkes; Esme J Baan; Maria A J de Ridder; Elisabeth Svensson; Daniel Prieto-Alhambra; Francesco Lapi; Carlo Giaquinto; Gino Picelli; Nada Boudiaf; Frank Albers; Lee A Evitt; Sarah Cockle; Eric Bradford; Melissa K Van Dyke; Robert Suruki; Peter Rijnbeek; Miriam C J M Sturkenboom; Hettie M Janssens; Katia M C Verhamme Journal: Pediatr Allergy Immunol Date: 2020-03-20 Impact factor: 6.377