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Development of Compendium for Esophageal Squamous Cell Carcinoma
03:36

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Published on: April 12, 2024

Quality evaluation of value sets from cancer study common data elements using the UMLS semantic groups.

Guoqian Jiang1, Harold R Solbrig, Christopher G Chute

  • 1Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA. jiang.guoqian@mayo.edu

Journal of the American Medical Informatics Association : JAMIA
|April 19, 2012
PubMed
Summary

This study developed a quality evaluation method for clinical research data using semantic web tools. It found that common data elements spanning multiple Unified Medical Language System semantic groups often indicate misclassification, aiding quality assurance.

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Last Updated: May 23, 2026

Development of Compendium for Esophageal Squamous Cell Carcinoma
03:36

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Published on: April 12, 2024

Area of Science:

  • Biomedical Informatics
  • Clinical Data Management
  • Semantic Web Technologies

Background:

  • Clinical research relies on standardized Common Data Elements (CDEs) for data integrity.
  • Accurate terminological annotations within CDE value sets are crucial for data quality.
  • Existing methods for evaluating CDE terminological quality can be labor-intensive.

Purpose of the Study:

  • To develop and evaluate a novel approach for assessing the quality of terminological annotations in CDE value sets.
  • To leverage Unified Medical Language System (UMLS) semantic types and groups for automated quality evaluation.
  • To establish a high-throughput mechanism for quality assurance in large clinical study data repositories.

Main Methods:

  • Integrated National Cancer Institute (NCI) CDEs, NCI Thesaurus (NCIt) concepts, and UMLS semantic network using a semantic web framework.
  • Employed SPARQL-enabled evaluation to identify CDE values and their meanings.
  • Assessed if meanings mapped to a single UMLS semantic group or spanned multiple groups.

Main Results:

  • Over a quarter (26.2%) of enumerated CDEs in the NCI Cancer Data Standards Repository utilized elements from more than one UMLS semantic group.
  • Analysis of a sample (n=100) revealed that 17% of these multi-group elements were potentially misclassified.
  • The presence of multiple semantic groups within a CDE value domain served as an indicator for potential quality issues.

Conclusions:

  • Semantic web tools provide an efficient, high-throughput method for evaluating the quality of extensive CDE collections.
  • Identifying CDEs with value domains spanning multiple UMLS semantic groups is an effective trigger for quality auditing.
  • This approach offers a valuable quality assurance mechanism for clinical study CDE repositories.