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Determining whether one concept (or expression) is a kind of another concept (or expression) is the fundamental capability enabled by SNOMED CT. For example, answering the question 'Which patients have an infectious disease?' involves finding all the patients with any kind of infectious disease (e.g. viral pneumonia, tuberculosis).

Subsumption occurs when one clinical meaning is a subtype of another clinical meaning, and testing for this is called 'subsumption testing'. If clinical meaning X is a subtype of clinical meaning Y, then Y is said to 'subsume' X and X is 'subsumed by' Y.

Subsumption testing between concepts is represented using a stated or implied |is a| relationship. For example, 75570004 |viral pneumonia| is a 40733004 |infectious disease| and therefore 40733004 |infectious disease| subsumes 75570004 |viral pneumonia|, and 75570004 |viral pneumonia| is subsumed by 40733004 |infectious disease|.

Subsumption testing between expressions tests to see if the candidate expression (often recorded in a patient record) is subsumed by a predicate expression (typically part of the query being run across the patient record). For example:

Candidate expression: 75570004 |viral pneumonia|

Predicate expression: 40733004 |infectious disease|:

363698007 |finding site| = 39607008 |lung structure|

In this case, the candidate expression is subsumed by the predicate expression.

Subsumption testing can be represented using the SNOMED CT Expression Constraint Language using the '<' (descendantOf) or '<<' (descendantOrSelfOf) operators. For example, the expression constraint:

<< 40733004 |infectious disease|

is satisfied by any expression that is subsumed by 40733004 |infectious disease|.

There are a variety of ways to implement subsumption testing. These are summarized in Section 0the Implementation sub-section below.

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Example

A typical example using subsumption would be an audit within a hospital, reviewing all patients with an infectious disease. In this scenario, the following simple query could be executed to find all the patients whose health record contains a diagnosis that is subsumed by the concept 40733004 |infectious disease|:

SELECT distinct patientID

FROM health_records

WHERE diagnosis = (<< 40733004 |infectious disease|)

If the health records contained the following data:

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Note that patient 456872 would not be returned by this query as neither 22298006 |myocardial infarction| or 195967001 |asthma| are subtypes of 40733004 |infectious disease|.

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In this approach, a description logic reasoner (e.g. Snorocket, ELK, Fact++) is used to determine whether one concept is subsumed by another.

In most environments, the recommended approach is to either use a precomputed transitive closure table or a description logic reasoner. However, where disk capacity or distribution bandwidth are limiting factors, branch numbering provides an efficient alternative approach. For more information on these approaches, please refer to Subtype search scope restriction  in the SNOMED CT Terminology Services Guide.

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In this approach, a description logic reasoner (e.g. Snorocket, ELK, Fact++) is used to determine whether one expression is subsumed by another.

Where available, the recommended approach is to use a description logic reasoner to calculate subsumption between expressions. However, comparing normal form expressions provides an alternative approach when a reasoner is not available. For more information on these approaches, please refer to Expression Retrieval and Normal Forms in the SNOMED CT Terminology Services Guide.

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A number of vendor products use the SNOMED CT hierarchy to support subsumption testing in their analytics services, including the Cerner Millennium Terminology (CMT) package (case study 2.8) and Epic's decision support and reporting tools (case study 2. 11). Terminology servers that provide the ability to perform subsumption testing include B2i Healthcare's Snow Owl® terminology server (case study 2. 5). The UK Terminology Centre's Data Migration Workbench also uses subsumption testing in its query tool, and its case mix and caseload trends analysis tools (case study 1.2).

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