When performing data analytics over clinical data, it is important to understand the interdependency between the terminology and the structural information model. For example, it is not sufficient to find a diagnosis of 56265001 |heart disease|, and make the assumption that the patient has heart disease. Instead, the surrounding information model must be considered to discover whether this is, for example, a confirmed diagnosis for the patient themselves, a suspected or preliminary diagnosis for the patient, or perhaps a family history of heart disease in the patient's paternal grandfather. Contextual or qualifying information about a code may appear in a variety of places, including:
The challenge often becomes even greater when heterogeneous data sources are integrated. When different information models represent the same semantics using different combinations of structure versus terminology, retrieval and reuse may miss similar information. To avoid false negatives or false positives in the query results, the integration and/or analytics processes must resolve these differences.
For example, in Figure in
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SNOMED CT is in the unique position to be able to resolve many of these challenges, using the techniques described in sections 6.4 Description Logic Over Terminology and 6.5 Description Logic Over Terminology and Structure. For example, SNOMED CT enables the computation of equivalence and subsumption between alternative representations of data. For example, the postcoordinated expression
22253000 |pain|: 363698007 |finding site| = 56459004 |foot|
(which can be represented either in a single data element or using two separate data elements for 22253000 |pain| and 56459004 |foot|) can be automatically determined to be equivalent to the precoordinated concept 47933007 |foot pain| (stored in a single data element).
Some cases exist, however, where SNOMED CT is not currently able to automatically establish equivalence. These cases primarily relate to concepts for which the SNOMED CT concept model does not yet fully model their meaning. For example, the two approaches for representing a 'twin pregnancy' shown below (