Mapping and matching concepts

From Discovery Data Service
Jump to navigation Jump to search

Mapping API

The information model mapping API is used, when given contextual data, to return both a property concept (representing the context) and a value concept (representing the value within that context). The structure of the mapping module allows multiple contexts to reference a common mapping node (for example when the same type of value is received from multiple sources) and also multiple value types to be referenced from a common mapping node.

The logic of the version 1 live mapping is described more fully in the model mapping article.

Managing codes and taxonomies

Information consists of ideas. Another word for an idea is a 'concept' . A concept may be named,( in which case the meaning of the concept can usually be understood), or they may be an unnamed expression, which is made up of a set of interrelated named or unnamed concepts.

For example the term "chest pain" implies the idea of a pain in the chest. In Snomed-CT it is a named concept. "Chest pain, worsened by exercise" may be an example of an expression style concept made up from the concept of "chest pain", and the statement that it is "made worse by -> exercise". In this case “made worse by” and “exercise” are both different concepts but no author has yet created a single named concept for this expression.

The new generation of health record management systems tend towards the recording of concepts, with the objective being for the record entry to closely match the idea behind the entry. These types of concepts can be called term based concepts as the term is the thing that describes the idea.

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 which can result in a computer deducing additional knowledge over and above the human who created the concept. Snomed-CT is the worlds largest ontology of healthcare term based concepts and is authored using DL. A collection of concepts defined in this way constitute an "Ontology" and there is a standard language OWL that is used to represent the definitions.

These types of concepts are referred to as "Core concepts"

The idea of codes originated from a different starting point. The intention of a coded entry is to pre-classify an entry before it is recorded. The code is designed for a particular set of business processes e.g. analytics or payment and it is important to understand the context in which a code has been used. A coded concept, being pre-classified, relies on categorisation of the codes, and that classification may or may not imply that one code is a subtype of another. Nothing can be inferred from a code other than its relation to another code as authored. Consequently, as the philosophy is different, code based concepts have to be dealt with differently from term based concepts, even if they seem to saying the same thing.

Because of their history, it is not always possible to assert the exact meaning of a code. However, it is often the case that meaning can be inferred or approximated from a coded entry. With preference to move to an ontology, this inference can be achieved via the use of a mapping process that matches coded concepts to term based concepts that are identified from a code.

These types of concepts are referred to as "legacy concepts"

There are two strategies to link legacy code concepts to core concepts.

1. A coded term may be stated confidently to be the same as, or a variation on, a concept. Typically code systems like Read2 or CTV3 can be dealt with in this way because they are designed to try and capture the idea in the clinicians mind, and they have been incorporated as concepts anyway. Likewise many system supplier codes have been created in this way. In this case the term code can be said to be a term code of the concept. Read2 G33 - Angina pectoris is a term code for the concept of angina pectoris.

2. A coded term might be the same term as a concept but may have been entered without the assertion that is a true representation of a state. Typically code systems such as ICD10 and OPCS fall into this category. E11 - Diabetes type 2, seems to be the same as the concept of diabetes type 2, but was entered without clinician attestation and may have been approximated for payment purposes. In this case a legacy concept is produced and a map between this concept and the similar clinical concept is generated.

A map is just another form of relationship, but unlike an ontological equivalent or subclass axiom it implies that the relationship is an approximation. It is a sort of statement that something is possibly or probably similar to something else and thus has much less weight than an asserted relationship.

Legacy Code based concepts can be mapped to Core concepts , and this enables the use of the vast volumes of data already recorded in systems. Maps must be used with care as it is almost always the case that the use of a mapped code in a query is dependent on the purpose of the query. This means that mappings are more of a guide to the things to include rather than a confident statement of meaning. When querying records the query author may need to determine which codes to include or exclude on a case by case basis.

Maps between core concepts and legacy concepts

As mentioned above the relationships are managed as mappings which state the type or degree of match.

Maps generally fall into 2 patterns. These are illustrated in the context of code based concepts as follows:

Simple match

A core concept may be matched to many code based concepts. In a simple match the legacy concept is deemed to be probably equivalent to, or a subclass of. the code concept

sn:194828000 |Angina (disorder)
    :matchedTo emis:G33 |Angina Pectoris|.

Complex optional match

A concept may be matched to a number of alternative concepts and it is expected that a query author may wish to select these.

In this example, the concept : "Ketoacidotic coma due to diabetes mellitus (disorder)" has a complex map which is selection of either

a) Coma unspecified


b) one of either Diabetes mellitus in pregnancy: Pre-existing diabetes mellitus, unspecified, or Diabetes mellitus in pregnancy, unspecified, or Diabetes mellitus arising in pregnancy

In effect meaning that the compound entry in the record would need to have 2 icd 10 codes to fulfill the criteria.

  :hasMap [
       :combinationOf  [ 
                           :oneOf  icd10:R402 ] 
                           :oneOf  icd10:O24.3 icd10:O24.9 O24.4]

Mapping source fields

It is equally common to need to map source fields to core data model properties. In the information model a property is considered just another concept.

However, to use mappings for source fields it is necessary to use the context in which the source fields exist.

In the above examples, coded concepts were considered as context independent in the sense that the same code used by many providers and many systems would generally mean the same thing and can be treated the same way.

In the same way that codes can be mapped, so can source resource types such as tables or fields, message types or message segments. Mapping may involve functional transformation

Defining source context

The first step in managing source concepts is to define the concept in the context of the originator of the data. This employs the use of a context object usually sent as parameters through the REST API. For example the following

  "organisation": "Barts",
  "system" : "CernerMillenium",
  "message" :"cds_type_130",
  "field" :"admin_cat_code"

Mapping nodes

A second step is for the mapping author to identify whether the source context is equivalent to another source contexts. This is done in order to rationalise the number of mappings steps needed between a source concept and the final target concept.

Matching to concept

The third step involves creation of a source concept and a core concept. In the above example the source is concept has been mapped to a core concept and the core concept is returned

  rdfs:label "admin_cat_code"

and potential core map



The information model has fully defined the administrative category property as a property of a subclass of encounter dealing with hospital stays. Consequently the source system's table and field can be fully mapped to the common model field.

Legacy codes, terms and term codes

Legacy or local codes also require context and the same approach is used as described above. In this case a local code 12345655 mat have different meaning in a ddifferent system. A local code may or may not have any sort of code scheme (the scheme then being implied by the context). In the following example, Barts trust has its own local cerner code scheme.

  "organisation": "Barts",
  "system" : "CernerMillenium",
  "message" :"cds_type_130",
  "field" :"admin_cat_code",
   "codeScheme" :"BartsLocalCodes",
  "code" :123445556

Enumerated values

The NHS Data dictionary employs enumerated numeric values for many of its field contents.

These are treated as local codes using the context to create a field specific code scheme. In many cases local trusts have extended the national scheme (or even changed the scheme), and as a result mapping nodes are used for the common codes.

Decision process for handling codes and terms

When incorporating codes and terms into the ontology, there are 4 categories of approach, the selected approach being dependent on the semantic relationship between the legacy and core concept. The categories are:

  1. Creation of a local concept and creation of a simple "matched to" map between a core concept and a legacy concept. This implies that the legacy concept is a subtype of, or equivalent to the core concept. Examples of these are supplier local codes, Read 2, TPP
  2. Creation of a local or legacy concept and a complex "combination of" + some of or one of "mapped to" between a core concept and a legacy concept. This implies a more nuanced relationship meaning that the user may elect how to use the maps when querying records.
  3. Mapping of the local context object to a mapping node which maps to a core concept i.e. the core concept is used throughout the data store and the local context is replaced.
  4. Mapping of the local context object to a mapping node which maps to a legacy concept which may or may not map from a core concept