By Pavel Klinov, Dmitry Mouromtsev
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The MCI [8] provides the basis for data integration and knowledge exploration . It defines the most important structural concepts (classes and relations) for the representation of clinical data. This is, in the first place structured data about the patient like diagnoses and findings, as well as provided examinations, procedures and therapies. The MCI is based on selected upper- and midlevel ontologies from the OBO library and reuses established schemas like the Dublin Core. It is used in combination with large reference terminologies such as RadLex or the Foundational Model of Anatomy (FMA) to represent clinical findings from unstructured data resources.
Types: On the one hand, we model the case that a single target is annotated as simple annotation model as illustrated in Figure 4. On the other hand, we also model annotations that associate a sequence of multiple consecutive tokens (a phrase) with a multi-target annotation. As illustrated in Figure 5, we use the composite element of the OA ontology to model the multitude of tokens, because the order of the tokens is not of importance. As seen in the example, no matter whether the text sequence is axillary lymph node or lymph node axillary, it is annotated using the same body (radlex:RID1517).
They enable users to formally describe a domain of knowledge while they can also share their data with the other members of the Semantic Web community in the form of OWL [1] and RDFS [2] documents. However, due to their independent design and the different purposes they serve, they are often highly heterogeneous which in turn prevents their seamless integration and use by relevant systems. Additionally, when their size increases significantly, they need a large amount of computing resources, while typical operations, such as searching and updating datasources, require a considerable amount of time for their execution.