Semantic Node Labeling
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To solve the problems introduced when conflicting logic types exist in clinical information models, explicit labeling of model components for intensional logic (by means of Semantic Node Labeling, SNL) can improve machine level interoperability and enable semantic web tools to be used safely.
1. IntroductionOur first white paper on logic in clinical modeling described serious operational problems that occur when clinical information models confuse intensional and extensional logic without a boundary between the different types of logic in the model. Most clinical models based on the HL7, openEHR, and ISO 13606 families of standards use Object Oriented (OO) extensional logic based on the "Closed World Assumption" (CWA). These information models usually have query characteristics that are incompatible with the intensional logic used in SNOMED CT which is based on the "Open World Assumption" (OWA).
Interoperability of clinical information between systems and between organizations requires explicit knowledge about where each logic type exists, i.e., to be explicit about when each logic type may be used. This paper proposes a method of identification of logic in clinical information models that may be used in multiple standards and modeling paradigms, and which would make it safe for SNOMED CT users to adopt the models by unambiguously labeling the model components in which the specific logic types reside.
1.1 About clinical information in SNOMED CTSNOMED CT is based upon the Semantic Web technologies . To query data in these types of logic one does not use SQL, nor any other object query languages. Instead the intensional logic requires that queries are executed using software classifiers which are also known as tableaux reasoners, reason engines, rules engines, or simply "reasoners."
Many reasoners are available that are used in clinical decision support, clinical research, and clinical analysis with SNOMED CT. Using this type of logic reduces cost, adds value, and dramatically reduces the technical and operational effort to produce clinical analytical results by allowing for the inference of new information that mathematically and logically follows from the stated axioms of SNOMED CT and the clinical information model. Reasoners that are used with SNOMED CT cannot be used in any part of a clinical information model that is based on the extensional OO type of logic.
1.2 About Clinical Information ModelsAbstract information models usually must account for the "what, where, who, why, when, and how" of the information they intend to represent. When SNOMED CT is used in clinical documentation and other medical information, generally it represents the "what." As noted above, clinical information in SNOMED CT cannot be processed effectively or efficiently if it is all mixed up with other data that are based on a fundamentally different system of logic; but how can one separate the wheat from the chaff? One way to maintain this separation would be to create a clinical statement that would represent the "where, who, why, and when" in an OO extensional model component, and strictly prohibit the "what" from being represented in this OO part whenever SNOMED CT is used. The Observation class of a clinical statement has an attribute named Code. If it is agreed to allow only SNOMED CT or a SNOMED CT extension in this "Code" attribute, and further specify that it must come from only certain hierarchies within SNOMED CT (for example Observables and Clinical Findings), then one could safely use a reasoner to perform analyses using powerful subsumption queries on the model. For example one easily could find all patients that have an "autoimmune disease with finding site lung and morphology fibrosis". A model designed this way is "correct" and one could safely obtain the benefits of using reasoners in clinical work.
Some models incorrectly and inappropriately mix up the two different kinds of logic, which presents problems that may have multiple solutions. The main problem can be most easily recognized by the use of "what" words like "Systolic Blood Pressure" in the extensional OO part of an information model that also may use SNOMED CT. This is an incorrect mixing of logic types because, in this case, Systolic Blood Pressure is a term defined in SNOMED CT that also is defined anew in an extensional model. Although it may be an unintentional result from the perspective of the model author, this model is reinventing SNOMED CT while simultaneously creating confusion and seriously complicating its use. Instead, the official definition of the "what" of the model should be taken from a standard vocabulary code bound to the object, e.g. SNOMED CT.
One possible alternative solution might be to use entirely intensional logic in all parts of a model; but this does not work primarily because available ontologies are not intended to be used for e.g. the "who" and "when" parts of models, and because with current technology they do not perform well for millions of patient records. On the other hand, models in which the use of SNOMED CT is prohibited do not have this problem. Generally, it could pose real dangers to patients and clinicians if the SNOMED CT term and the newly defined term were confused. It would be best never to use such "what" words in a clinical model that also may use SNOMED CT, but when they remain, then we propose they always should be an interface term or human readable label for the concept. Much of the model may be completed in the extensional OO style, but the "what" part usually should use an ontology like SNOMED CT and this must be known explicitly.
2. A new proposed solution, Semantic Node Labelling (SNL)The case above describes an example of the explicit separation of logic types in a model by an informal rule. The extensional logic – the OO part – is the base of the model, and the intensional logic would be limited to only one or more nodes in the model, in this case the "code" of the Observation class. If it were also known to the modelers and the users of this model that only SNOMED CT would be allowed in this part of the clinical information model, and only specific SNOMED CT hierarchies, then the users of the model would know that reasoners and powerful subsumption searches may be used, but that they must be limited to SNOMED CT-compatible logic and only in that one designated node of the model.
We can generalize this principle by explicit labeling of the model components, or Semantic Node Labeling (SNL). Explicit labeling would greatly simplify the analysis of models and enable machine interoperability of the SNOMED CT information without special knowledge of the intent of the author of the model. Users of SNOMED CT and others who would like to take advantage of the power of semantic web technologies like SPARQL and OWL, or the EL+ intensional logic of SNOMED CT, could do so without danger.
In SNL we propose that clinical information models incorporate conditional and optional metadata tags that may be bound to any node in any clinical model:
3. ConclusionUsers of clinical information models who take advantage of intensional logic with tableaux reasoners and other ontology classifiers have a recognized problem: there sometimes is no way to know when, if, or how such logic can be safely used except by having special knowledge of the intent of the author of a model. This is because there is no mutually agreed way to separate the two kinds of logic that occur in clinical models, and any mutually agreed way to do this in one particular model would not carry over to other models.
A small set of metadata tags can conditionally and optionally be attached to any model component or node in any extensional OO based clinical information model to designate the node as capable of being reasoned over with intensional logic tools (such as SNOMED CT subsumption for example). These metadata tags could be attached to any node in a model, but we anticipate these labels will mainly, at least in the beginning, be attached to the "what" nodes in clinical statement models. We call these tags Semantic Node Labeling or SNL.
The proposed tags for Semantic Node Labelling are:
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