=Paper=
{{Paper
|id=Vol-1428/BDM2I_2015_paper_2
|storemode=property
|title=Developing a Modular Architecture for Creation of Rule-based Clinical Diagnostic Criteria
|pdfUrl=https://ceur-ws.org/Vol-1428/BDM2I_2015_paper_2.pdf
|volume=Vol-1428
|dblpUrl=https://dblp.org/rec/conf/semweb/HongJPC15
}}
==Developing a Modular Architecture for Creation of Rule-based Clinical Diagnostic Criteria==
https://ceur-ws.org/Vol-1428/BDM2I_2015_paper_2.pdf
Developing a Modular Architecture for Creation of Rule-
based Clinical Diagnostic Criteria
Na Hong1,2, Guoqian Jiang1*, Jyotishman Pathak1, Christopher G Chute3
1
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; 2 Institute of
Medical Information, Chinese Academy of Medical Sciences, Beijing, China; 3 Johns Hopkins
University, Baltimore, MD, USA
Abstract. With recent advances in computerized patient records system, there
is an urgent need for producing computable and standards-based clinical diag-
nostic criteria. For example, constructing rule-based clinical diagnosis criteria
has become one of the goals in the International Classification of Diseases
(ICD)-11 revision. However, few studies have been done in building a unified
architecture to support the need for diagnostic criteria computerization. In this
study, we present a modular architecture for creation of rule-based clinical di-
agnostic criteria leveraging Semantic Web technologies. The architecture con-
sists of two major modules: one is an authoring module that utilizes a standards-
based information model and the other is a translation module that utilizes Se-
mantic Web Rule Language (SWRL). In a prototype implementation, for the
authoring module, we developed a diagnostic criteria upper ontology that inte-
grates ICD-11 content model with Quality Data Model (QDM); for the transla-
tion module, we developed a transformation tool that converts QDM-based di-
agnostic criteria into Semantic Web Rule Language (SWRL) representation.
We evaluated the domain coverage of the upper ontology model by annotating
20 randomly selected diagnostic criteria. We also tested the transformation al-
gorithms using 6 QDM templates for ontology population and 15 QDM-based
criteria data for rule generation. In summary, our efforts in developing and pro-
totyping a modular architecture provide useful insights into building a scalable
solution to support diagnostic criteria representation and computerization.
Keywords: Diagnostic Criteria, Ontology, ICD-11, QDM, SWRL
1 Introduction
Diagnostic criteria are one of the most valuable sources of knowledge for supporting
clinical decision-making and improving patient care [1], [2], [3], [4]. The clinical
informatics research community has been seeking a solution to standardize and com-
puterize clinical diagnosis criteria for all clinical domains. Diagnostic criteria are
usually scattered over different media such as medical textbooks, literatures and clini-
cal practice guidelines mostly in textual formats. Many studies have been conducted
*
Correspondence to: Dr. Guoqian Jiang at jiang.guoqian@mayo.edu.
in integrating and formally expressing diagnostic rules from free-text-based clinical
guidelines and diagnostic criteria into computerized decision support system to im-
prove clinical performance and patient outcomes [5], [6]. However, very limited re-
search has been done on building a unified architecture to support the goal of diagnos-
tic criteria formalization. In particular, the lack of a standards-based information
model has been recognized as a major barrier for achieving computable diagnostic
criteria[7]. Diagnostic criteria are usually described in different narrative style, granu-
larity, term usage and inner logic. There is a need to develop a clear information mod-
el specification and a standard architecture to support the diagnostic criteria modeling
and representation, and thereby enabling computerization. To achieve a unified archi-
tecture, the following aspects should be considered: a) an information model that
supports standard representation of diagnostic criteria; b) the semantic interoperability
and expressivity of a knowledge representation language; c) the rule-based reasoning
capability over the fact knowledge; and d) a standard exchange format for different
layers of the architecture.
Current efforts in the development of international recommendation standard mod-
els in clinical domains have laid the foundation for modeling and representing com-
putable diagnostic criteria. The notable examples include the International Classifica-
tion of Diseases (ICD)-11 content model [8], [9] and National Quality Forum (NQF)
Quality Data Model (QDM)1. The content model of ICD-11 is a structured framework
that defines “a classification unit” in ICD in a standard way in terms of its compo-
nents that allows computerization. Under the definition of the content model, each
ICD entity can be seen from different dimensions and there are currently 13 defined
main dimensions in the content model. One purpose of the ICD-11 content model is to
use different settings of these dimensions or parameters to construct different sets of
diagnostic criteria, so different elements in the content model come together to define
the diagnosis criteria of a particular ICD category. As the ICD-11 content model de-
picts a big picture of diagnostic criteria computerization and it has achieved consen-
sus among the ICD Revision Group, we consider it a viable framework on which to
build our Diagnostic Criteria Upper Ontology (DCUO).
The QDM is an information model that describes clinical concepts in a standard-
ized format to enable electronic quality performance measurement in support of oper-
ationalizing the Meaningful Use Program of the Health Information Technology for
Economic and Clinical Health Act. It allows quality measure developers and many
clinical researchers or performers to describe clearly and unambiguously the data
required to calculate the performance measure. As the purpose, QDM allows electron-
ic health records (EHR) and other clinical electronic system to share a common un-
derstanding and interpretation of the clinical data. To describe different part of the
clinical care process, QDM defines many datatypes to specify the context in which
each category is used. It has been proved that the extension of QDM could support a
number of relevant areas. As a standard format, Health Quality Measure Format
(HQMF) [10] formally defines a quality measure (data elements, logic, definitions,
1
http://www.healthit.gov/quality-data-model
etc.) to support consistent and unambiguous interpretation. HQMF has been accepted
as a format to define eMeasures in the HL7 standard.
While formalizing the inner logic for diagnostic criteria is complex, Semantic Web
technologies provide a homogeneous framework that enables an ontology-based mod-
eling with the Web Ontology Language (OWL)2 and supports rule-based reasoning
with the Semantic Web Rule Language (SWRL) [11]. In a semantic web environ-
ment, OWL is a W3C recommendation for ontology description and modeling and
SWRL is a rule language to formalize and represent rules to support knowledge rea-
soning. In the present study, we evaluate OWL and SWRL-based representation lan-
guages for formalizing diagnostic criteria.
The objective of the present study is to describe our efforts in developing a modu-
lar architecture for creation of rule-based clinical diagnostic criteria leveraging Se-
mantic Web technologies. We prototyped and evaluated a number of key components
of the architecture, including an upper ontology and a transformation tool. We select a
collection of QDM datatypes that are commonly used in describing diagnostic criteria
and then integrated them into ICD-11 Content Model to build a schema for a diagnos-
tic criteria upper ontology. We perform our data translation and interaction following
the HQMF standard format and propose extensions where needed.
2 Materials & Methods
2.1 Materials
WHO ICD-11 content model: WHO developed a content model to present the
knowledge that underlies the definitions of an ICD entity [8]. The content model is
composed of three layers: a foundation layer, a linearization layer, and an ontological
layer. The foundation layer is the core product of the ICD-11 revision that stores the
full range of knowledge of all classification units in ICD.
Each ICD entity can be seen from different dimensions. The content model represents
each one of these dimensions as a parameter. Currently, there are 13 defined main
parameters in the content model to describe a category in ICD-11, for example, Mani-
festation Properties, Causal Properties, Treatment Properties. “Diagnostic Criteria” is
one of the main parameters for describing an ICD category.
NQF Quality Data Model (QDM): QDM consists of two modules: a data-model
module and a logic module. The data-model module includes the notions of category
(e.g., Medication), datatype (e.g., Medication, Administered), attribute (e.g., infor-
mation about dosage, route, strength, and duration of a medication), and value set
comprising concept codes from one or more terminologies. The logic module includes
Logic Operators, Functions, Comparison Operators, Temporal Operators, Subset Op-
erators. As mentioned above, the HQMF provides a standard format to render the
QDM-based criteria (i.e., instance data) in XML format using a collection of tem-
plates [10]. In a previous study [12], we evaluated the feasibility of using QDM for
representing diagnostic criteria through a data-driven approach and suggested that the
2
http://www.w3.org/TR/owlfeatures/
common patterns informed by QDM are useful and feasible in building a standards-
based information model for computable diagnostic criteria. In this study, we refer-
ence the common patterns and selected a collection of QDM datatypes and attributes
for developing an upper ontology.
2.2 Methods
The overall system architecture for creation of rule-based clinical diagnosis criteria is
shown in Figure 1.
Fig. 1. Overall System Architecture for Creation of Rule-based Clinical Diagnosis Criteria
The system architecture contains two major modules: one is an authoring module that
utilizes a standards-based information model and the other is a translation module that
utilizes SWRL. The first module of the architecture contains an upper ontology that
supports the organization of diagnostic criteria. We integrated a collection of selected
ICD-11 content model elements and QDM elements manually informed by the analy-
sis of real-world diagnostic criteria. The first module also contains a unified web user
interface that supports collecting and authoring diagnostic criteria from clinicians or
experts. All collected data elements, value sets and logic expressions of diagnostic
criteria are formalized using QDM-based HQMF template. Standard QDM model
serves as a foundation layer for all following automatic parsing and reasoning work.
The second module of the architecture contains a rule translation engine that converts
diagnostic criteria represented in QDM-based HQMF templates into domain-specific
diagnostic criteria ontology and a set of rules using SWRL. The rule translation en-
gine supports further diagnostic inference on patient data. In the following subsec-
tions, we mainly focus on describing the core parts that we prototyped and developed
in detail.
2.2.1 Developing a standards-based diagnostic criteria upper ontology
The purpose of this work is to integrate existing standard information models rele-
vant to modeling of diagnostic criteria by expert review and manual editing. As men-
tioned in the section above, we choose the ICD-11 content model and NQF QDM as
reference standards. Our work in this stage is to create a diagnostic criteria upper
ontology (DCUO) through integration of ICD-11 content model and those QDM ele-
ments commonly used in diagnostic criteria. The selection of these QDM elements
was informed by the results from a previous study [12]. We selected 10 QDM
datatypes and 4 QDM attributes and integrated them with ICD-11 content model-
based ontology schema. Table 1 shows a list of the QDM datatypes and attributes
used for the integration. We used Protégéontology editing environment for manually
integrating these two standard information models into a diagnostic criteria upper
ontology.
Table 1. A list of selected QDM datatypes and attributes for developing the upper ontology
QDM Datatypes QDM Attributes
Laboratory Test, Result Result
Diagnostic Study, Performed Method
Diagnostic, Active Reason
Physical Exam, Performed Severity
Symptom, Active
Medication, Active
Patient Characteristic Birth Date
Patient Characteristic Race
Patient Characteristic Sex
Procedure, Recommended
2.2.2 Transforming QDM templates into domain-specific diagnostic criteria on-
tology
To build a scalable diagnostic rule translation environment, it is important to dy-
namically populate a Diagnostic Criteria Domain Ontology (DCDO) for a specific
disease or condition, e.g. ‘DCDO for AMI (Acute Myocardial Infarction)’. We devel-
oped a parsing interface that could support data extraction from diagnostic criteria
encapsulated by HQMF templates. To parse all HQMF instance data in a specific
template, we developed a collection of JAVA-based XML parsing and mapping algo-
rithms to automatically extract instance data from HQMF templates and convert them
into corresponding DCDO elements. The parsing algorithms decompose HQMF XML
data into different parts and populate the parsed elements into the same DCDO. The
process of the ontology population consists of 2 steps: template-based XML parsing
and semantic mapping.
A HQMF template example and its parsing results are shown in Figure 2. The left-
hand part is the template representation of QDM datatype “Laboratory Test, Result”
(hqmf r1 template - 2.16.840.1.113883.3.560.1.12) [10] and the right-hand part is the
elements extracted from the XML template.
Elements of template - 2.16.840.1.113883.3.560.1.12
“30954-2”
“Results”
“2.16.840.1.113883.6.1”
“$valueSetOID”
“2.16.840.1.113883.3.560.101.1”
“$displayName”
“$datatypeName”
“$valueSetName”
Fig. 2. An XML Parsing of the HQMF template “Laboratory Test, Result” (hqmf r1 template -
2.16.840.1.113883.3.560.1.12)
And then, we created semantic mapping between the XML elements and the elements
of the DCDO ontology. For example, the semantic mappings of the template -
2.16.840.1.113883.3.560.1.12 are shown in Table 2.
Table 2. Semantic mappings between HQMF template elements and ontology elements
Elements of template - Elements of Ontology
2.16.840.1.113883.3.560.1.12
“30954-2” Annotation property of “Laboratory Test, Result”
“Results” Annotation property of “Laboratory Test, Result”
“2.16.840.1.113883.6.1” Annotation property of “Laboratory Test, Result”
“$valueSetOID” Annotation property of “$valueSetName”
“2.16.840.1.113883.3.560.101.1” Annotation property of “$valueSetName”
“$displayName” Annotation property of “$valueSetName”
“$datatypeName” Class: Laboratory Test, Result
“$valueSetName” Class: Subclass of “$datatypeName”
2.2.3 Automatic rule composition and validation
After having a DCDO ontology populated, we developed JAVA-based algorithms
using Protégé OWL API and SWRL API for automatic rule composition and rule
validation, which are respectively responsible for rule assembling and rule grammar
checking.
The SWRL syntax contains two parts: Body and Head. The Body is also called the
antecedent and the Head part is the consequent of the rule. There are 6 atom types that
can be used as the components of the Body and Head: class atom, individual property
atom, same/different atom, and data valued property atom, build-in atom and data
range atom.
Adhering to SWRL structure and grammar, we designed a collection of translation
algorithms to automatically extract SWRL rule elements from the logic components
of an HQMF XML template and then to assemble these rule elements into the SWRL
syntax.
For example, Figure 3 shows the HQMF XML representation of the QDM-based
criterion “Laboratory Test, Result: LDL-c (result < 100 mg/dL)”. The criterion is
composed by two templates: HQMF template “Laboratory Test, Result” (hqmf r1
template - 2.16.840.1.113883.3.560.1.12) and HQMF template “result comparison”
(hqmf r1 comparison template - 2.16.840.1.113883.3.560.1.1019.3).