Pacific Northwest University of Health Sciences, Yakima, WA, USA.
Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA.
J Am Med Inform Assoc. 2019 Feb 1;26(2):149-154. doi: 10.1093/jamia/ocy161.
Mappings between ontologies enable reuse and interoperability of biomedical knowledge. The Radiology Gamuts Ontology (RGO)-an ontology of 16 918 diseases, interventions, and imaging observations-provides a resource for differential diagnosis and automated textual report understanding in radiology. An automated process with subsequent manual review was used to identify exact and partial matches of RGO entities to the Disease Ontology (DO) and the Human Phenotype Ontology (HPO). Exact mappings identified equivalent concepts; partial mappings identified subclass and superclass relationships. A total of 7913 distinct RGO entities (46.8%) were mapped to one or both of the two target ontologies. Integration of RGO's causal knowledge resulted in 9605 axioms that expressed direct causal relationships between DO diseases and HPO phenotypic abnormalities, and allowed one to formulate queries about causal relations using the abstraction properties in those two ontologies. The mappings can be used to support automated diagnostic reasoning, data mining, and knowledge discovery.
本体之间的映射支持生物医学知识的重用和互操作性。Radiology Gamuts Ontology（RGO）是一个包含 16918 种疾病、干预措施和影像学观察结果的本体，为放射学中的鉴别诊断和自动化文本报告理解提供了资源。我们使用自动过程和后续的手动审查来识别 RGO 实体与疾病本体（DO）和人类表型本体（HPO）之间的精确和部分匹配。精确映射识别等效概念；部分映射识别子类和超类关系。共有 7913 个不同的 RGO 实体（46.8%）被映射到两个目标本体中的一个或两个。RGO 的因果知识的整合产生了 9605 个公理，这些公理表达了 DO 疾病和 HPO 表型异常之间的直接因果关系，并允许使用这两个本体中的抽象属性来制定关于因果关系的查询。这些映射可用于支持自动化诊断推理、数据挖掘和知识发现。
