=Paper=
{{Paper
|id=Vol-1302/paper6
|storemode=property
|title=Ontology Patterns for Clinical Information Modelling
|pdfUrl=https://ceur-ws.org/Vol-1302/paper6.pdf
|volume=Vol-1302
|dblpUrl=https://dblp.org/rec/conf/semweb/Martinez-CostaKS14
}}
==Ontology Patterns for Clinical Information Modelling==
https://ceur-ws.org/Vol-1302/paper6.pdf
Ontology Patterns for Clinical Information Modelling
Catalina Martínez-Costa1, Daniel Karlsson2, Stefan Schulz1
1
Institute for Institute for Medical Informatics, Statistics and Documentation,
Medical University of Graz, Austria
2
Department of Biomedical Engineering, Linköping University, Sweden
{catalina.martinez,stefan.schulz}@medunigraz.at,
Daniel.Karlsson@liu.se
Abstract. Motivated by our experiences of representing clinical information us-
ing OWL DL, which often resulted in highly complex expressions, we propose
the use of ontology content patterns to facilitate this task. They are based on a
set of formal ontologies, constrained by the concepts and relations of a top-level
one, which reduces arbitrariness in ontology design. We propose their applica-
tion to information encoded by electronic health records specifications and on-
tology-based terminologies, in order to provide semantic interoperability across
heterogeneously represented data, and to guide the creation of clinical models
and detect semantic inconsistencies across them. We provide examples of their
application to achieve the above mentioned tasks and discuss the limitations and
further research issues.
Keywords: ontology content patterns, electronic health standards, SNOMED
CT
1 Introduction
Despite a wide-spread use of computers in clinical documentation, the semantic in-
teroperability of information kept in electronic health record (EHR) systems is insuf-
ficient [1]. A plurality of EHR representations together with medical terminologies
like SNOMED CT [2], have been proposed in recent years to structure clinical infor-
mation and to provide standardized codes for frequently used medical terms, respec-
tively.
Existing EHR standards and medical terminologies were developed in isolation and
major problems exist when they are combined. Projects such as the HL7 TermInfo [3]
or more recently the Clinical Information Modeling Initiative (CIMI) [4] and the Eu-
ropean network SemanticHealthNet [5], have attempted to provide solutions by ad-
dressing the lack of division between ontology-based medical terminologies and in-
formation models (provided by EHR representations). This is commonly known as
the boundary problem [6].
TermInfo provides a set of rules for the combined use of the HL7 information
model and SNOMED CT; CIMI proposes a set of modelling patterns, defined as clin-
ical models that are intended to act as guide for the creation of new ones. Clinical
models constrain information model structures to represent particular data capture and
communication use cases. In medicine it is often not possible to impose one universal
data form, such as for recording diagnostic information. Thus, CIMI associates each
clinical model with a set of iso-semantic models (models heterogeneously structured
but with the same meaning), from which one is selected as the preferred one and
mappings are established across them.
CIMI or HL7 based models that implement the TermInfo specification might work
well in isolation, but semantic interoperability issues arise when interacting with oth-
ers, which are not necessarily compatible, whilst the anticipation of all possible iso-
semantic representations will lead to an explosion of models. The European network
SemanticHealthNet addresses this problem by providing clinical model information
structures with a set of expressions, based on a shared ontological framework. This
framework allows representing both (ontology-based) medical terminologies and
information models, and implements the classical distinction between ontology [7]
(what exists – independently of being known or observed) and epistemology [8] (what
is known, suspected, planned, etc.).
The inherent complexity of this representation is addressed by using semantic pat-
terns as intermediate representations, which is the focus of this paper.
2 Background
2.1 EHR Structured Clinical Models
Several EHR standards and specifications propose representing clinical infor-
mation by using clinical models based on a reference information model (RM). Clin-
ical models, also known as archetypes (e.g. openEHR/ISO 13606 archetypes) [9,10]
or HL7 CDA documents [11], constrain a set of standardized information structures
provided by some reference model (RM), to represent EHR data. They are used for
modeling particular use cases for clinical data capture and communication. As an
example, the ISO 13606 archetype of Fig. 1 constrains information structures (e.g.
CLUSTER, ELEMENT, etc.) to represent a medical questionnaire consisting of ques-
tions groups. The use of terminologies and ontologies within clinical models is known
as terminology binding. Fig. 1 shows how the information structure
ELEMENT[at0003] is bound to the SNOMED CT concept Past history of diabetes
mellitus. Interpreted within the context of the clinical model, it is a question, and its
allowed answers are yes / no.
In practice, the division line between ontologies and information models is often
crossed both by ontologies (where they represent epistemic and temporal information
aspects, such as “known present” or “past history of”) and by RMs and clinical mod-
els (where they carry their own ontology without reference to external standards, here
the fact that it is a question).
ENTRY[at0000] matches { -- Question group
items matches{
CLUSTER[at0001] matches { -- Question group 417662000 | past history of clinical finding | : {
items matches { 246090004 | associated finding | =
CLUSTER[at0002] matches { -- Question 73211009 | diabetes mellitus | }
items matches {
terminology binding
ELEMENT[at0003] matches{ -- Answer
value matches {
BL matches {True, False}
…}}}}}}}}
Fig. 1. (Left) ISO 13606 archetype excerpt to record questionnaire; (Right) Binding of an in-
formation structure to a SNOMED CT concept.
2.2 Ontology-based medical terminologies: SNOMED CT
Ontologies formally describe properties and relations of types of entities. Domain-
independent categories, relations and axioms are typically provided by top-level on-
tologies [12], whereas the types of things that make up a domain are represented by
domain ontologies. In the former one we find categories like Process, Material entity,
Quality, etc., whereas in a clinical domain ontology we would find Diabetes mellitus
type 1, Left index finger, or Aspirin, i.e. the classes of entities corresponding to the
terms used in clinical documentation and reporting, and defined by the properties
shared by all of their individual members.
Medical terminologies have evolved in the last years to include definitional
knowledge about their terms, by using an ontological framework in order to help hu-
mans and computers to recognize the intended meaning of their terms, for proper
coding of, retrieval of, and inferencing about biomedical data, as well as for mainte-
nance of the terminology itself [13]. An example is SNOMED CT, a clinical termi-
nology covering all aspects of clinical medicine, with about 300,000 representational
units (called SNOMED CT concepts) and terms in several languages.
Due to the legacy of its predecessors, SNOMED CT does not only provide codes
for clinical terms proper but also for contextual statements, which are often represent-
ed in information models. An example of this is the Situation with explicit context
concept hierarchy (i.e. context model), in which we find terms such as Suspected deep
vein thrombosis or No past history of venous thrombosis. We have largely harmonized
the SNOMED CT content with basic top-level classes and relations of BioTopLite
upper ontology [14] (e.g. btl:Process, btl:Quality, btl:Condition, btl:Situation, etc), in
order to better distinguish clinical from information entities. Based on [15] we inter-
pret SNOMED CT concepts from the clinical finding hierarchy as clinical situations
and reinterpreted the SNOMED CT context model [16]. Fig. 2 shows the OWL DL
representation of a post-coordinated1 expression that follows the context model and
represents past history of diabetes. Past history is a temporal aspect that specializes
the meaning of the finding diabetes mellitus.
1
Post-coordination describes the representation of a term using a combination of two or more
of them (e.g. past history of clinical finding and diabetes mellitus)
‘past history of clinical finding (situation)’
and RoleGroup some (
(‘Associated finding (attribute)’ some ‘Diabetes mellitus (disorder)’) and
(‘Finding context (attribute)’ some ‘Known present (qualifier value)’) and
(‘Temporal context’ some ‘In the past (qualifier value)’) and
(‘Subject relationship context’ some ‘Subject of record (person)’))
Fig. 2. OWL DL SNOMED CT representation of an expression based on the post-coordination
of two terms (past history of clinical finding and diabetes mellitus linked by the linkage
concept associated finding). Terms using italics represent ontology classes, bold face repre-
sents ontology object properties.
3 Methods
A shared OWL DL [17] ontological framework is proposed that allows relating EHR
information models with medical terminologies [18] in an unambiguous way. It is
supported by a the use of semantic patterns in order to provide semantic interoperabil-
ity across heterogeneously represented data and to guide the creation of clinical mod-
els and detect semantic inconsistencies across them.
3.1 Clinical Information Semantic Patterns
The semantic patterns we propose represent recurrent clinical information model-
ling aspects and can therefore be considered ontology design content patterns applied
to clinical information. They are inspired by the experience of modelling clinical in-
formation based on ontologies. As ontology patterns they help to reduce the arbitrari-
ness that exists when representing clinical information, by using a set of OWL DL
formal ontologies as standard modelling framework [19].
Two ontologies, the SNOMED CT ontology (prefix sct) and an information ontol-
ogy (prefix shn) are rooted in the biomedical top-level ontology BioTopLite (prefix
btl). The use of BioTopLite standardizes the ontology development process, by
providing a set of logical axioms which constrain how both ontologies are related. We
use SNOMED CT as common reference point for representing the healthcare domain.
The information ontology provides a set of classes that represent contextual and tem-
poral information aspects (e.g. diagnostic information, past history, provisional, etc.)
and refer to the SNOMED CT concepts.
Each pattern can be considered a small ontology based on the previous framework,
to be used as a building block for a particular modelling use case. For that, they can
be specialized and composed by following similar principles to object oriented lan-
guages [20].
According to [21], content patterns are language-independent and should be en-
coded in a high order representation language. Nevertheless, their representation in a
logic-based language allows the use of DL reasoning [22], which can be used to en-
sure the consistency of the patterns and to allow inference-related tasks. On the left
side, Fig. 3 shows the graphical representation of a pattern that represents the past
history of some patient clinical situation. The right side, shows a concrete instance of
that pattern that represents the statement “Past history of diabetes mellitus”. Other
examples of patterns are “Family history of clinical situation” or “Plan to perform
some clinical process”.
Fig. 3. (Left) Graphical representation of the history-situation pattern; (Right) Instance of the
history-situation pattern; Squares represent ontology classes and unidirectional arrows predi-
cates enhanced by cardinality constraints.
Within SemanticHealthNet, we have elaborated two representations of semantic
patterns: an OWL 2 DL and a RDF [23] representation. The OWL-based representa-
tion describes a pattern as a set of logical axioms. Table 1 shows the OWL rendering
of the history-situation pattern as pieces of information (shn:InformationItem) that are
acquired by performing some clinical process (shn:ClinicalProcess) and that refer to
clinical situations (shn:ClinicalSituation) of a given type (if any), which happened in
the past (sct:InThePast). Additionally, it allows expressing epistemic information
aspects (shn:InformationAttribute) that indirectly refer to the situation (e.g. severe,
present, etc.).
shn:InformationItem
and shn:isAboutSituation only shn:ClinicalSituation
and btl:isOutcomeOf some shn:ClinicalProcess
and shn:hasInformationAttribute some shn:InformationAttribute
and shn:hasInformationAttribute some sct:InThePast
and shn:hasInformationAttribute some sct:FindingContextValue
Table 1. OWL DL representation of history-situation pattern; Terms using italics represent
ontology classes, bold face represents ontology object properties.
Table 2 shows the RDF representation, which consists of a set of Subject-Predicate-
Object (SPO) triples. Both representations are connected as follows: The subject and
object parts of a triple correspond to ontology classes, and the predicates to ontology
expressions. Table 3 provides the OWL DL translation of the RDF predicates. This
allows the implementation of automatic translations from a ‘closer to user’ RDF rep-
resentation into a representation in OWL DL, which would require a more in-depth
understanding of DL syntax and semantics. In the following we will describe the use
of semantic patterns regarding EHR clinical models and ontology-based terminologies
as SNOMED CT.
shn:InformationItem ´describes situation´ shn:ClinicalSituation
shn:InformationItem ´results from process´ shn:ClinicalProcess
shn:InformationItem ´has attribute´ shn:InformationAttribute
shn:InformationItem ´has temporal context´ sct:InthePast
shn:InformationItem ´has situation context´ sct:FindingContextValue
Table 2. Subject-Predicate-Object (SPO) triple representation; Italic terms represent ontology
classes and terms in quotes represent predicates. Note that predicates are not equivalent to
OWL object properties.
Predicate OWL DL expression
‘describes situation’ SUBJ subClassOf shn:isAboutSituation only OBJ
‘results from process’ SUBJ subClassOf btl:isOutcomeOf some OBJ
‘has attribute’ SUBJ subClassOf shn:hasInformationAttribute some OBJ
‘has temporal context’ SUBJ subClassOf shn:hasInformationAttribute some OBJ
´has situation context´ SUBJ subClassOf shn:hasInformationAttribute some OBJ
Table 3. Translations of RDF predicates into OWL DL axioms within the shared ontology
framework
3.2 The role of semantic patterns regarding EHR clinical models and medical
domain ontology-based terminologies
Assuming that a limited set of top-level semantic patterns that can be specialized
and composed is sufficient to represent a great variety of clinical information, we
propose the use of semantic patterns as proxy to the semantic representation of clini-
cal information encoded by EHR structured clinical models and ontology-based med-
ical terminologies. They act as a template, with fix and variable parts, and guide the
mapping process in which the correspondences between information model structures
and their values are defined with regards to the ontology. Dashed arrows in Fig. 4
indicate the correspondences between the clinical model from Fig. 1 and the history-
situation pattern.
As observed, the pattern is applied to both, the SNOMED CT term used as binding
and the clinical model information structures. Three correspondences have been pro-
vided. Two between the CLUSTER[at0002] binding and the pattern triples that repre-
sent the situation and its temporal context. Diabetes mellitus is placed as subclass of
shn:ClinicalSituation. One between the value of ELEMENT [at0003] and the pattern
triple that represents if the situation is present (sct:KnownPresent) or absent
(sct:KnownAbsent). Both are represented as subclasses of sct:FindingContextValue,
and will be selected depending of the value of the model instance (True or False).
417662000 | past history of clinical finding | : {
246090004 | associated finding | =
73211009 | diabetes mellitus | }
ENTRY[at0000] matches { -- Question group
items matches{
CLUSTER[at0001] matches { -- Question group
items matches {
CLUSTER[at0002] matches { -- Question
items matches { terminology binding
ELEMENT[at0003] matches{ -- Answer
value matches {
BL matches {True, False}
…
}}}}}}}}
Fig. 4. (Left) ISO 13606 archetype and SNOMED CT binding to record the question “past
history of diabetes” (Y/N); (Right) Graphical representation of the history-situation pattern
4 Results
In the following we will describe the potential of semantic patterns for each of the
tasks introduced in the Methods section. We will use the history-situation pattern as
example.
4.1 Semantic patterns provide interoperability across heterogeneously
represented data
We will use the history-situation pattern to provide semantic interoperability across
two past history data instances captured by two heterogeneous fictitious applications
used at a GP consultation and at a hospital. Fig. 5 shows their interfaces. They have
been designed attending to different requirements and therefore record the infor-
mation at different levels of detail. At the hospital (right), the specialist records addi-
tional information about the patient past situation (i.e. cause and severity). However,
the GP only records the situation itself (left).
Fig. 5. (Left) Past history recording at the GP; (Right) Past history recording at the specialist.
Each of the above applications is based on a different ISO 13606 clinical model. The
GP application is based on the questionnaire model introduced in Section 2.1. The left
part of Fig. 6 shows the model used by the hospital application. Both are different in
terms of structure but not syntax, since both implement the same standard.
In order to access information recorded by both applications, independently of their
source representation, the correspondences between each clinical model and the histo-
ry-situation pattern are defined. Fig. 4 depicted the correspondences between the
questionnaire model and the pattern. Following, dashed arrows in Fig. 6 show the
correspondences for the hospital model. This model allows recording the severity of
the past disease and its cause, requiring the use of the situation pattern, by composi-
tion. The situation pattern, allows providing more detail information such as when it
occurs, where, associated situations, etc.
Once the correspondences between the models and the patterns are established,
when the former ones are instantiated with patient data, the instances of the patterns
are also created, in a similar way to the one shown in Fig. 3. If OWL DL instances are
created, it is possible to perform homogeneous queries on instances from both appli-
cations and retrieve their results [24].
ENTRY[at0000] matches {-- Past history
items matches{
ELEMENT[at0001] matches{ -- Condition
value matches {
CODED_TEXT matches {*} }
CLUSTER[at0002] matches { -- Details
items matches {
ELEMENT[at0001] matches{ -- Cause
value matches {
CODED_TEXT matches {*} }}}
ELEMENT[at0001] matches{ -- Severity
value matches {
CODED_TEXT matches {*} }
}}
Fig. 6. ISO 13606 clinical model that records past history of condition, its cause and severity
Besides, the use of the ontology framework and DL reasoning allows performing
queries at different granularity level: E.g. “Information about all patients with past
history of some endocrine disease”, without specifying whether diabetes or a different
one.
4.2 Semantic patterns guide the creation of clinical models and detect
semantic inconsistencies
Semantic patterns can guide the development of new clinical models if the latter
are created by following the constraints dictated by a set of limited top-level patterns.
Top-level patterns are based on a set of generic ontology classes and predicates
that can be specialized and composed by following the ontology constraints. These
constraints can be used to determine which elements include in a clinical model or in
a terminology binding.
As a difference with clinical models, where their elements are only structurally re-
lated (e.g. list, tree, etc.), within patterns they are connected by semantic relationships
(e.g. shn:isAboutSituation, btl:isOutcomeOf, etc.). These relationships can be used to
guide the decision of the elements to include in a model, reducing the existing arbi-
trariness. Now this is mainly a non-constrained modeller decision that might lead to
the creation of non-interoperable models even for the same use case.
If semantic patterns are not applied at clinical models design time, they still can be
used to detect semantic inconsistencies across them. As an example, Fig. 7 shows an
excerpt of a CIMI model that records observation results. It records: (i) what is ob-
served, ELEMENT[at0001] (e.g. color of the eye); (ii) the reason to perform the ob-
servation, ITEM[at0002] (e.g. problem wearing contact lens); (iii) the method used to
observe, ITEM[at0003] (e.g. eye examination); (iv) the status of the observation,
ELEMENT[at0004] (e.g. performed, planned); and (v) the priority to perform the
observation, ELEMENT[at0005] (e.g. high, normal).
CLUSTER[at0000] matches { -- Observable
item matches {
ELEMENT[at0001] occurrences matches {1} matches { -- Name
value matches { TEXT matches {*}}}
ITEM[at0002] occurrences matches {0..*} -- Reason
ITEM[at0003] occurrences matches {0..*} -- Method
ELEMENT[at0004] occurrences matches {0..1} matches { -- Status
value matches { CODED_TEXT matches {*}}}
ELEMENT[at0005] occurrences matches {0..1} matches { -- Priority
value matches { TEXT matches {*}}}
}}
Fig. 7. Excerpt of the CIMI model (CIMI-CORE-CLUSTER.observable.v1.0.0) to record ob-
servation results
Fig. 8 shows another CIMI model that records observation requests and references the
above model by composition (keyword “use_archetype”). Besides, it also references a
model to record observation actions. Within this last model we have found a content
overlapping with the observation result one, since it also provides elements for re-
cording the reason, method, status and priority of the observation.
ENTRY[at0000.1] matches { -- Observation
link matches {LINK[at0.1] occurrences matches {0..*} -- Associated request}
data matches {
use_archetype CLUSTER [CIMI-CORE-CLUSTER.observable.v1] -- Observable
use_archetype CLUSTER [CIMI-CORE-CLUSTER.finding.v1] -- Results
use_archetype CLUSTER [CIMI-CORE-CLUSTER.observe_action.v1] -- Observe action
…
Fig. 8. Excerpt of the CIMI model (CIMI-CORE-ENTRY.observation.v4.0.0) to record an
observation request and its result
Semantic patterns could avoid such an overlapping situation, by providing formal
modelling guidelines, based on the ontological framework, to distinguish across what
is observed, the observation procedure and the result of the observation.
Additionally, as already mentioned, they can help to guide or detect inconsistencies
regarding terminology bindings. For instance, the pattern logic axiom
(shn:InformationItem and shn:isAboutSituation only shn:ClinicalSituation), relates
an information entity (i.e. shn:InformationItem) with a clinical entity
(shn:ClinicalSituation) and the latter is equivalent to SNOMED CT clinical findings.
Therefore, if a model information structure is mapped to that axiom, its value is only
valid if it is of the type clinical finding.
When clinical models are instantiated with patient data, semantic patterns can also
be used to check that the data entered complies with the constraints defined at the
model level.
5 Discussion and conclusions
In this work we have proposed semantic patterns as ontology design content pat-
terns applied to the representation of clinical information. They were motivated by
our experiences of representing clinical information using OWL DL, which often
resulted in highly complex expressions.
The EHR standards community has put a lot of effort in providing standardized
means to represent the EHR. However, the complexity of the medical domain and
their heterogeneous data capture and re-use needs does not make it easy. One of the
reasons might be the high degree of freedom provided when modelling clinical infor-
mation, which is mainly formally constrained in terms of structure but without con-
sidering the meaning of what is being represented.
Aware of this gap, and concerned about the need of providing standardized model-
ling means, we propose an ontological framework, in order to represent both infor-
mation and medical entities, constrained by a top-level ontology which reduces arbi-
trariness in ontology design. Semantic patterns are based on this framework and there-
fore constrained by their concepts and relations. In [25], the advantages of using a
top-level ontology for creating ontology design content patterns were described, stat-
ing that it provides it with an existing backbone structure and well-defined relations.
Semantic patterns provide a more intuitive representation and standardize their de-
velopment process, yet allowing flexibility through specialization and composition.
We have proposed their representation in OWL DL and in RDF. The former one al-
lows logical reasoning and therefore more advanced exploitation of information, alt-
hough it might be more difficult to implement in a real system, due to performance
issues. In the latter case, the RDF representation although less expressive and there-
fore more limited in terms of information exploitation, might be more adequate. Cor-
respondences between both representations exist, what might allow using the most
suitable one for each use case.
In this work we have demonstrated how semantic patterns can be applied to EHR
clinical models and ontology-based terminologies (1) to provide semantic interopera-
bility across heterogeneously represented data and (2) commented their potential use
to guide the creation of clinical models and detect semantic inconsistencies across
them.
By looking at the content patterns available at the NeOn repository [26], we did not
find specific patterns for the modelling of clinical information. However, patterns
such as the agent-role or the action ones can be applied.
There are numerous new issues that arise from the use of semantic patterns for
EHR modelling that still have to be investigated. These include the selection of the
right set of patterns to be used for modelling specific pieces of clinical information,
who would create and maintain the patterns and who would manage and validate
them.
Other issues must be further investigated, such as providing evidence that a set of
top-level semantic patterns for modelling clinical information can be rather small,
with increasing complexity and expressiveness coming from specialization and com-
position. So far we have only worked with limited modelling examples and we need
more evidence of the real benefit of using patterns; what is hard to obtain without
appropriate tools that implement them.
Further research should include the potential of semantic patterns for detecting se-
mantic inconsistencies across existing clinical models, considering their specializa-
tion, composition and cardinality constraints. Languages such as SPIN [27] or RDF
shapes [28] could be helpful for their representation and are subject of our research.
Acknowledgements. This work has been funded by the SemanticHealthNet Net-
work of Excellence within the EU 7th Framework Program, Call:FP7-ICT- 2011-7,
agreement 288408. http://www.semantichealthnet.eu/
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