Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT
Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT
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DOI：
10.1007/s10278-019-00186-3
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发表时间：
2019-04-01
影响因子：
4.4
通讯作者：
Kahn, Charles E., Jr.
中科院分区：
文献类型：
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作者：
Filice, Ross W.;Kahn, Charles E., Jr.
本体提供了领域中概念及其之间关系的人类可读和机器可计算的表示。本体之间的映射使生物医学知识的重用和互操作性成为可能。我们试图将放射学色域本体（RGO）的概念映射到临床放射学的三个关键词汇表中的术语，RGO是一种将疾病和成像发现联系起来以支持放射学中的鉴别诊断的本体：国际疾病分类第10版临床修订版（ICD-10-CM）、北美放射学会放射学词典（RadLex）和医学临床术语系统化命名法（SNOMED CT）。RGO（版本0.7; 2018年1月）纳入了16，918个疾病，干预措施和成像观察的术语（类别），由1782个包含（类别-子类）关系和55，569个因果（可能原因）关系联系起来。使用国家生物医学本体中心（NCBO）注释器网络服务将RGO类映射到RadLex（46，656类，版本3.15）、SNOMED CT（347，358类，版本2018 AA）和ICD-10-CM（94，645类，版本2018 AA）。我们确定了1275个从RGO到RadLex的精确映射，5302个到SNOMED CT，941个到ICD-10-CM。RGO术语映射到一个本体（n = 3401）、两个本体（n = 1515）或所有三个本体（n = 198）。映射的本体提供附加术语以支持从电子健康记录中的文本信息进行数据挖掘。目前的工作建立在将RGO映射到疾病和表型本体的努力之上。本体之间的映射可以支持自动化知识发现、诊断推理和数据挖掘。
An ontology offers a human-readable and machine-computable representation of the concepts in a domain and the relationships among them. Mappings between ontologies enable the reuse and interoperability of biomedical knowledge. We sought to map concepts of the Radiology Gamuts Ontology (RGO), an ontology that links diseases and imaging findings to support differential diagnosis in radiology, to terms in three key vocabularies for clinical radiology: the International Classification of Diseases, version 10, Clinical Modification (ICD-10-CM), the Radiological Society of North America's radiology lexicon (RadLex), and the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). RGO (version 0.7; Jan 2018) incorporated 16,918 terms (classes) for diseases, interventions, and imaging observations linked by 1782 subsumption (class-subclass) relations and 55,569 causal (may cause) relations. RGO classes were mapped to RadLex (46,656 classes, version 3.15), SNOMED CT (347,358 classes, version 2018AA), and ICD-10-CM (94,645 classes, version 2018AA) using the National Center for Biomedical Ontology (NCBO) Annotator web service. We identified 1275 exact mappings from RGO to RadLex, 5302 to SNOMED CT, and 941 to ICD-10-CM. RGO terms mapped to one ontology (n = 3401), two ontologies (n = 1515), or all three ontologies (n = 198). The mapped ontologies provide additional terms to support data mining from textual information in the electronic health record. The current work builds on efforts to map RGO to ontologies of diseases and phenotypes. Mappings between ontologies can support automated knowledge discovery, diagnostic reasoning, and data mining.