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Integrating ontologies of human diseases phenotypes and radiological diagnosis

机译:整合人类疾病的本体表型和放射学诊断

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摘要

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.
机译:本体之间的映射使得生物医学知识的重用和互操作性。放射域属性本体论(RGO)-AN本体论1618个疾病,干预和成像观察 - 为放射学中的差异诊断和自动化报告理解提供了一种资源。具有后续手动审查的自动化过程用于将RGO实体与疾病本体(DO)和人类表型本体(HPO)的精确和部分匹配标识。确切的映射确定了等同的概念;部分映射确定了子类和超类关系。总共7913个不同的RGO实体(46.8%)被映射到两个目标本体中的一种或两种。 Rgo的因果知识的整合导致了9605个公理,表达了Do疾病和HPO表型异常之间的直接因果关系,并允许其中使用这两个本体中的抽象属性进行关于因果关系的查询。映射可用于支持自动诊断推理,数据挖掘和知识发现。

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