Mapping and matching concepts

A general language consists of a vocabulary of words arranged according to a syntax that follows grammatical rules.

Information consists of ideas. Another word for ideas is a 'concept' . A concept may be named, in which case the meaning of the concept can usually be understood by the name, or they may be unnamed (an "expression") which is a definition made up from other named or unnamed concepts. The term "chest pain" implying the idea of a pain in the chest is one example of a named concepts. "Cheat pain, worsened by exercise" is an example of an expression style concept made up from the concept of "chest pain", the statement that it is "made worse by" and the statement that it was made worse by "exercise".

The new generation of health record management systems tend towards the recording of concepts with an objective to closely match the idea behind the entry. These types of concepts are often called term based concepts.

A modern term based concept is defined in relation to other concepts by a set of assertions indicating whether the concept is equivalent to, or a subtype of, a set of other concepts. The standard approach to this is via the use of Description Logic (DL). By using DL, a computer can automatically classify a concept. Snomed-CT is the worlds largest ontology of term based concepts.

Coded concepts, originate from a different starting point. The intention of a coded entry is to pre-classify an entry before it is recorded. The classification marker or code, is designed for a particular set of business processes e.g. analytics or payment. A coded concept, being pre-classified, relies on categorisation of the codes, which may or may not imply subtypes. Consequently, as the philosophy is different, code based concepts have to be dealt with differently from term based concepts.

Because of their history, it is not always possible to assert the exact meaning of a code based concept. However, it is often the case that meaning can be inferred or approximated from a coded entry. This inference is achieved via the use of maps.

A map is a sort of statement that something is possibly or probably similar to something else. It has much less weight than an asserted relationship. Code based concepts can be mapped to term based concepts which enable the use of the vast volumes of data already recorded in systems. Maps generally fall into 4 patterns. These are illustrated in the context of code based concepts as follows:

• A coded concept has one map which is mapped to one term based concept with a certain weighting or map category. For example the icd10 code for Angina has a map which maps to a single term based Snomed-CT concept of angina, with a category indicating that the source concept is properly classified. Note that many coded concepts may be mapped to one single term based concept. The map is viewed from the perspective of the coded concept.

icd10:I209 |Angina Pectoris (ICD10 I20.9| 
            has map [mappedTo sn:194828000 |Angina (disorder);
            mapCategory sn:447637006 |Map source concept is properly classified]

A coded concept has more than one map and each map may map to ore than one potential term based concept

The approach taken in Discovery is to classify according to "sets" and thus adopting the approach taken by modern ontologies. A set is a definition of a set of things that have the same properties ( i.e. a class and a set are the same thing). Sets of ideas may contain other subsets which are objects that have the same and more specific properties than the super set (or super class) i.e. a subclass of a superclass.

Putting together RDF and sets, the net result aligns with RDF, RDFS and OWL2 i.e. the ontology web language. The vocabulary of OWL2 is used to precisely define concepts in relation to other concepts. OWL2 uses an underlying idea of "Description Logic" which is a way of defining things in a logical and consistent way so that a classification can be reliably produced.