<unk>  consists of four primary core components: # Concept Codes – numerical codes that identify clinical terms, primitive or defined, organized in hierarchies # Descriptions – textual descriptions of Concept Codes # Relationships – relationships between Concept Codes that have a related meaning # Reference Sets – used to group Concepts or Descriptions into sets, including reference sets and cross-maps to other classifications and standards.<unk>  "Concepts" are representational units that categorize all the things that characterize healthcare processes and need to be recorded therein. In 2011,<unk>  included more than 311,000 concepts, which are uniquely identified by a concept ID, e.g. the concept 22298006 refers to Myocardial infarction. All<unk>  concepts are organized into acyclic taxonomic (is-a) hierarchies; for example, Viral pneumonia IS-A Infectious pneumonia IS-A Pneumonia IS-A Lung disease. Concepts may have multiple parents, for example Infectious pneumonia is also a child of Infectious disease. The taxonomic structure allows data to be recorded and later accessed at different levels of aggregation.<unk>  concepts are linked by approximately 1,360,000 links, called relationships. Concepts are further described by various clinical terms or phrases, called Descriptions, which are divided into Fully Specified Names (FSNs), Preferred Terms (PTs), and Synonyms. Each Concept has exactly one FSN, which is unique across all of<unk> . It has, in addition, exactly one PT, which has been decided by a group of clinicians to be the most common way of expressing the meaning of the concept. It may have zero to many Synonyms. Synonyms are additional terms and phrases used to refer to this concept. They do not have to be unique or unambiguous. Semantic tag<unk>  assigns each concept a semantic tag. It is present in parenthesis in Fully Specified Name of each concept. There can be multiple semantic tags used within each<unk>  top level hierarchy. For example, top level hierarchy of Pharmaceutical/biologic Product uses semantic tags of: product, medicinal product, medicinal product form and clinical drug. Only one semantic tag can be used for each concept. The formal model underlying<unk> <unk>  statistics (as of November 2016). SD stands for sufficiently defined.<unk>  can be characterized as a multilingual thesaurus with an ontological foundation. Thesaurus-like features are concept–term relations such as the synonymous descriptions "Acute coryza", "Acute nasal catarrh", "Acute rhinitis", "Common cold" (as well as Spanish "resfrío común" and "rinitis infecciosa") for the concept 82272006. Under ontological scrutiny,<unk>  is a class hierarchy (with extensive overlap of classes in contrast to typical statistical classifications like ICD). This means that the<unk>  concept 82272006 defines the class of all the individual disease instances that match the criteria for "common cold" (e.g., one patient may have "head cold" noted in their record, and another may have "Acute coryza"; both can be found as instances of "common cold"). The superclass (Is-A) Relation relates classes in terms of inclusion of their members. That is, all individual "cold-processes" are also included in all superclasses of the class Common Cold, such as Viral upper respiratory tract infection (Figure). Common cold as a primitive concept in<unk> <unk> 's relational statements are basically triplets of the form Concept1 – Relationx – Concept2, with Relationx being from a small number of relation types (called linkage concepts), e.g. finding site, due to, etc. The interpretation of these triplets is (implicitly) based on the semantics of a simple Description logic (DL). E.g., the triplet Common Cold – causative agent – Virus, corresponds to the first-order expression forall x: instance-of (x, Common cold) -> exists y: instance-of (y, Virus) and causative-agent (y, x) or the more intuitive DL expression Common cold subClassOf causative-agent some Virus In the Common cold example the concept description is "primitive", which means that necessary criteria are given that must be met for each instance, without being sufficient for classifying a disorder as an instance of Common Cold . In contrast, the example Viral upper respiratory tract infection depicts a fully described concept, which is represented in description logic as follows: Viral upper respiratory tract infection as a defined concept in<unk>  Viral upper respiratory tract infection equivalentTo Upper respiratory infection and Viral respiratory infection and Causative-agent some Virus and Finding-site some Upper respiratory tract structure and Pathological-process some Infectious process This means that each and every individual disorder for which all definitional criteria are met can be classified as an instance of Viral upper respiratory tract infection. Description logics As of 2021,<unk>  content limits itself to a subset of the EL++ formalism, restricting itself to the following operators: *Top, bottom *Primitive roles and concepts with asserted parent(s) for each *Concept definition and conjunction but NOT disjunction or negation *Role hierarchy but not role composition *Domain and range constraints *Existential but not universal restriction *A restricted form of role inclusion axiom (xRy ^ ySz => xRz) *General Concept Inclusion axioms (A ⊆ B). For understanding the modelling, it is also important to look at the stated view of a concept versus the inferred view of the concept. In further considering the state view,<unk>  used in the past a modelling approach referred to as 'proximal parent' approach. After 2015, a superior approach called "proximal primitive parent" has been adopted. Precoordination and postcoordination<unk>  provides a compositional syntax that can be used to create expressions that represent clinical ideas which are not explicitly represented by<unk>  concepts. This mechanism exists because it is challenging to create and maintain all possible concepts upfront (as precoordinated concepts). For example, there is no explicit concept for a "third degree burn of left index finger caused by hot water". However, using the compositional syntax it can be represented as : = , = , = , = index finger structure Such expressions are said to have been 'postcoordinated'. Post-coordination avoids the need to create large numbers of defined Concepts within<unk> . However, many systems only allow for precoordinated representations. Reliable analysis and comparison of post-coordinated expressions is possible using appropriate algorithms machinery to efficiently process the expression taking account of the underlying description logic. Major Electronic Health Record Systems (EHRS) have repeatedly complained to IHTSDO and other standards organizations about the "complexity" of post-coordinated expressions. For example, the postcoordinated expression above can be transformed using a set of standard rules to the following "normal form expression" which enables comparison with similar concepts. : = , = : = ) , { = , = } Postcoordination is an important desirable feature of a terminology. Prior 2020, International Classification of Diseases (ICD) did not allow post-coordination and<unk>  was the only terminology that supported postcoordination. Since 2020, a new version of ICD-11 now also supports postcoordination. Veterinary content The International Edition of<unk>  only includes human terms. In 2014, clearly veterinary concepts were moved into a<unk>  veterinary extension. This extension is managed by the Veterinary Terminology Services Lab at the Va-Md College of Veterinary Medicine at Virginia Tech. Known deficiencies and mitigation strategies Earlier<unk>  versions had faceted structure ordered by semantic axes, requiring that more complex situations required to be coded by a coordination of different codes. This had two major shortcomings. On the one hand, the necessity of post-coordination was perceived as a user-unfriendly obstacle, which has certainly contributed to the rather low adoption of early<unk>  versions. On the other hand, uniform coding was difficult to obtain. E.g.,Acute appendicitis could be post-coordinated in three different ways with no means to compute semantic equivalences.<unk>  had addressed this problem by introducing description logic formula. With the addition of<unk> V3 a large number of concepts were redefined using formal expressions. However, the fusion with<unk> V3, as a historically grown terminology with many close-to user descriptions, introduced some problems which still affect<unk> . In addition to a confusing taxonomic web of many hierarchical levels with massive multiple inheritance (e.g. there are 36 taxonomic ancestors for Acute appendicitis), many ambiguous, context-dependent concepts have found their way into<unk> . Pre-coordination was sometimes pushed to extremes, so there are, for example, 350 different concepts for burns found on the head. A further phenomenon which characterizes parts of<unk>  is the so-called epistemic intrusion. In principle, the task of terminology (and even an ontology) should be limited to providing context-free term or class meanings. The contextualization of these representational units should be ideally the task of an information model. Human language is misleading here, as we use syntactically similar expression to represent categorically distinct entities, e.g. Ectopic pregnancy vs. Suspected pregnancy. The first one refers to a real pregnancy, the second one to a piece of (uncertain) information. In<unk>  most (but not all) of these context-dependent concepts are concentrated in the subhierachy Situation with explicit context. A major reason for why such concepts cannot be dispensed with is that<unk>  takes on, in many cases, the functionality of information models, as the latter do not exist in a
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