Scholarlink

<p>Modern healthcare interoperability demands objective methods for quantitatively evaluating the coverage and granularity of biomedical terminology systems to support evidence-based selection and integration decisions. We introduce novel metrics—structural size (an integrated measure of width and depth), mapping burden ratio (a measure of relative granularity between systems), and content overlap—to quantitatively evaluate the semantic integration potentials of five major terminology systems: SNOMED CT; Logical Observation Identifiers Names and Codes (LOINC); International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM); Gene Ontology (GO); and Current Procedural Terminology. The Unified Medical Language System Metathesaurus was employed to establish semantic equivalency between concepts from different systems. SNOMED CT exhibited superior granularity across most clinical domains, with some exceptions (ICD-10-CM in “Qualifier value,” GO in “Observable entity,” and LOINC in “Staging and scales.”) These findings address the challenge of semantic degradation in health information exchange by quantifying the degree to which meaning might be lost when translating between terminology systems. The proposed metrics empower healthcare organizations to develop targeted extensions or integration strategies that maintain semantic consistency across systems, providing objective tools for terminology system selection, integration planning, and semantic interoperability assessment.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Quantitative analysis of the comprehensiveness and granularity of biomedical terminology systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月3日

查看全文
  • Eunsuk Chang,
  • Sumi Sung

摘要

Modern healthcare interoperability demands objective methods for quantitatively evaluating the coverage and granularity of biomedical terminology systems to support evidence-based selection and integration decisions. We introduce novel metrics—structural size (an integrated measure of width and depth), mapping burden ratio (a measure of relative granularity between systems), and content overlap—to quantitatively evaluate the semantic integration potentials of five major terminology systems: SNOMED CT; Logical Observation Identifiers Names and Codes (LOINC); International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM); Gene Ontology (GO); and Current Procedural Terminology. The Unified Medical Language System Metathesaurus was employed to establish semantic equivalency between concepts from different systems. SNOMED CT exhibited superior granularity across most clinical domains, with some exceptions (ICD-10-CM in “Qualifier value,” GO in “Observable entity,” and LOINC in “Staging and scales.”) These findings address the challenge of semantic degradation in health information exchange by quantifying the degree to which meaning might be lost when translating between terminology systems. The proposed metrics empower healthcare organizations to develop targeted extensions or integration strategies that maintain semantic consistency across systems, providing objective tools for terminology system selection, integration planning, and semantic interoperability assessment.