=Paper=
{{Paper
|id=Vol-1320/paper_15
|storemode=property
|title=Building a Semantic Web-based Metadata Repository for Facilitating Detailed Clinical Modeling in Cancer Genome Studies
|pdfUrl=https://ceur-ws.org/Vol-1320/paper_15.pdf
|volume=Vol-1320
|dblpUrl=https://dblp.org/rec/conf/swat4ls/JiangSSTWC14
}}
==Building a Semantic Web-based Metadata Repository for Facilitating Detailed Clinical Modeling in Cancer Genome Studies==
https://ceur-ws.org/Vol-1320/paper_15.pdf
Building A Semantic Web-based Metadata Repository for
Facilitating Detailed Clinical Modeling in Cancer
Genome Studies
Guoqian Jiang1, Deepak K. Sharma1, Harold R. Solbrig1, Cui Tao2, Chunhua Weng3,
Christopher G. Chute1
1
Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN
{jiang.guoqian, sharma.deepak2, solbrig.harold, chute}@mayo.edu
2
University of Texas Health Science Center at Houston Houston, TX
cui.tao@uth.tmc.edu
3
Columbia University, New York City, NY
cw2384@cumc.columbia.edu
Abstract.
Detailed Clinical Models (DCMs) have been regarded as the basis for retain-
ing computable meaning when data are exchanged between heterogeneous
computer systems. To better support clinical cancer data capturing and report-
ing, there is an emerging need to develop informatics solutions for standards-
based clinical models in cancer study domains. The objective of the study is to
develop and evaluate a use case-driven approach that enables a Semantic Web-
based cancer study metadata repository based on both ISO11179 metadata
standard and Clinical Information Modeling Initiative (CIMI) Reference Model
(RM). We used the common data elements (CDEs) defined in The Cancer Ge-
nome Atlas (TCGA) data dictionary, and extracted the metadata of the CDEs
using the NCI Cancer Data Standards Repository (caDSR) CDE dataset ren-
dered in the Resource Description Framework (RDF). The
ITEM/ITEM_GROUP pattern defined in the latest CIMI RM is used to repre-
sent reusable model elements (mini-Archetypes). We performed a case study of
the domain “clinical pharmaceutical” in the TCGA data dictionary to demon-
strate the clinical utility of our approach. We produced a metadata repository
with 38 clinical cancer genome study domains, comprising a rich collection of
mini-Archetype pattern instances. In summary, our informatics approach lever-
aging Semantic Web technologies provides an effective way to build a CIMI-
compliant metadata repository that would facilitate the detailed clinical model-
ing to support use cases beyond TCGA in clinical cancer study domains.
Keywords. Detailed Clinical Models (DCMs), Clinical Information Model-
ing Initiative (CIMI), Common Data Elements (CDEs), The Cancer Ge-
nome Atlas (TCGA), Cancer Studies, Semantic Web Technologies
1 Introduction
Detailed Clinical Models (DCMs) have been regarded as the basis for retaining com-
putable meaning when data are exchanged between heterogeneous computer systems
(1). Several independent DCM initiatives have emerged, including HL7 DCMs (2),
ISO/CEN EN13606/Open-EHR Archetype (3), Intermountain Healthcare Clinical
Element Models (CEMs) (4), and the Clinical Information Model in the Netherlands
(5). The collective DCM community has recently initiated an international collabora-
tion effort known as the Clinical Information Modeling Initiative (CIMI) (6). The
primary goal of CIMI is to provide a shared repository of detailed clinical information
models based on common formalism.
While the primary focus of these DCM efforts has been on interoperability between
electronic health record (EHR) systems, there are also emerging interests in the use of
DCMs in the context of clinical research and broad secondary use of EHR data. A
typical use case is the Office of the National Coordinator (ONC) Strategic Health IT
Advanced Research Projects Area 4 (SHARPn) (7-8), in which the Intermountain
Healthcare CEMs have been adopted for normalizing patient data for the purpose of
secondary use. In the context of clinical research, for example, Clinical Data Inter-
change Standards Consortium (CDISC) intends to build reusable domain-specific
templates under its SHARE project (9-10).
To better support clinical cancer data capturing and reporting, there is an emerging
need to develop informatics solutions for standards-based clinical models in clinical
cancer study domains. For example, National Cancer Institute (NCI) has implemented
the Cancer Data Standards Repository (caDSR) (11), together with a controlled ter-
minology service (known as Enterprise Vocabulary Services – EVS), as the infra-
structure to support a variety of use cases from difference clinical cancer study do-
mains. NCI caDSR has adopted the ISO 11179 metadata standard that specifies a
standard data structure for a common data element (CDE) (12-13).
The use case in this study is based on The Cancer Genome Atlas (TCGA) Biospeci-
men Core Resource (BCR) data dictionary (14). The data dictionary is used to create
clinical data collection forms for different clinical cancer genome study domains.
TCGA clinical data include vital status at time of report, disease-specific diagnostic
information, initial treatment regiments and participant follow-up information (15).
The data dictionary groups a preferred set of CDEs per TCGA cancer study domain
and renders them as an XML Schema document. All clinical data collected are vali-
dated against these schemas, which provides a layer of standards-based data quality
control. All the CDEs are recorded in the NCI caDSR repository, the implementation
of which is based on the ISO 11179 standard. We envision the definition of a pre-
ferred set of CDEs for each clinical cancer study domain is analogous to the DCM
modeling effort.
The objective of the study is to develop and evaluate a use case-driven approach that
enables a Semantic Web-based metadata repository based on both ISO11179 metada-
ta standard and Clinical Information Modeling Initiative (CIMI) Reference Model
(RM). We first used the XML2RDF Transformation technology to transform TCGA
data dictionary and caDSR CDE dataset from XML format to RDF-based representa-
tion. This transformation allows us to use SPARQL queries to retrieve the caDSR
metadata elements that correspond to the CDEs defined in TCGA data dictionary. We
then transformed the CIMI Reference Model from UML to a corresponding OWL
representation and harmonized it with a subset of ISO 11179 metadata model, from
which, we extracted the ITEM/ITEM_GROUP patterns out of the data structures of
the CDEs in each TCGA cancer genome study sub-domain, and populated the pat-
terns as the instances of the CIMI Reference Model schema. Finally, we performed a
case study in a sub-domain clinical pharmaceutical to demonstrate clinical utility of
our proposed approach.
2 Materials and Methods
2.1 Materials
2.1.1 ISO 11179 and its OWL representations
ISO 11179 is an international standard known as the ISO/IEC 11179 Metadata Regis-
try (MDR) standard (12). It consists of six parts. Part 3 of the standard uses a meta-
model to describe the information modeling of a metadata registry, which provides a
mechanism for understanding the precise structure and components of domain-
specific models.
Figure 1 shows a diagram illustrating the high-level data description meta-model in
the ISO 11179 specification. The Data Element is one of foundational concepts in the
specification. ISO 11179 also specifies the relationships and interfaces between data
elements, value sets (i.e., enumerated value domains) and standard terminologies.
Several Semantic Web-based representations of the ISO 11179 Part 3 meta-model
have been created for projects including the XMDR project (16), Semantic MDR in a
European SALUS project (17) and CDISC2RDF in FDA PhUSE Semantic Technolo-
gy project (18). In the present study, we utilize a meta-model schema in OWL/RDF
developed in the CDISC2RDF project, which is a subset of ISO 11179 Part 3 meta-
model.
Figure 1. High-level data description meta-model in ISO 11179 specification
2.1.2 CIMI Reference Model in UML
The CIMI Reference Model (RM) is an information model from which CIMI’s clini-
cal models (i.e., archetypes) are derived (6). The CIMI DCM’s are expressed as for-
mal constraints on the underlying RM. The CIMI RM is represented in the Unified
Modeling Language (UML). The September 5, 2014 version of the CIMI RM (v2.0.1)
had four packages: 1) CIMI Core Model; 2) Data Value Types; 3) Primitive Types
and 4) Party. Core Model package includes the main classes in the CIMI RM. The
Data Value Types and Primitive Types packages defines the data types used in the
other two packages, The Party package defines the generic concepts of PARTY,
ROLE and related details that provide a flexible way for defining demographic attrib-
utes that may be required.
Figure 2 shows the Version v2.0.1 of CIMI Core Model. The classes ITEM,
ITEM_GROUP, and ELEMENT form a generic pattern that can be used to represent
a wide variety of clinical information. We will refer to this pattern as as the
“ITEM/ITEM_GROUP pattern”. ITEM is the abstract parent of both ITEM_GROUP
and ELEMENT. ITEM_GROUP represents the grouping variant of ITEM as an or-
dered list. ELEMENT represents a “leaf” ITEM which carries no further recursion.
Figure 3 shows Archetype Definition Language (ADL) (19) definition of a “Body
Temperature” archetype, which illustrates how ITEM_GROUP and ELEMENT can
be combined when representing a clinical concept.
2.1.3 caDSR CDE dataset
NCI caDSR is part of the NCI Cancer Common Ontological Representation Environ-
ment (caCORE) infrastructure and uses caCORE resources to support data standardi-
zation in cancer clinical research studies (11). The system includes an administrator
web interface for overall system and CDE management activities. Integrated with
caCORE Enterprise Vocabulary Services (EVS), the CDE Curation Tool aids devel-
opers in consumption of NCI controlled vocabulary and standard terminologies for
naming and defining CDEs.
NCI caDSR provides the ability to download CDEs in either Excel or XML format
(20), which we used to download an XML image of all non-retired production CDEs
(i.e., CDEs with Workflow status NOT = “RETIRED”) as of August 7, 2014. Figure 4
shows an XML rendering of the CDE “Pharmacologic Substance Begin Occurrence
Month Number”.
Figure 2. CIMI Core Model in UML Diagram
Figure 3. The definition section of an archetype for CIMI “Body temperature” con-
cept rendered in archetype definition language (ADL)
2.1.4 TCGA data dictionary
The Cancer Genome Atlas (TCGA), a joint venture supported by the NCI and the
National Human Genome Research Institute (NHGRI), is a comprehensive and coor-
dinated effort to accelerate the understanding of the molecular basis of cancer through
the application of genome analysis technologies, including large-scale genome se-
quencing. Being a component of TCGA Research Network, the Biospecimen Core
Resource (BCR) serves as the centralized tissue processing and clinical data collec-
tion center. A BCR data dictionary has been produced using the standard CDEs from
NCI caDSR. The CDEs in the data dictionary are publicly available in the XML for-
mat. In this project, we will download a snapshot of the data dictionary from the
TCGA website (14).
Figure 4. CDE “Pharmacologic Substance Begin Occurrence Month Number” in
XML.
Figure 5. The TCGA data definition of CDE “Pharmacologic Substance Begin Occur-
rence Month Number” in XML.
2.2 Methods
Figure 1 shows the system architecture of our proposed approach. The system
comprises four layers: a RDF transformation layer; a RDF store-based persistence
layer; a semantic services layer and an authoring application layer. This paper focuses
on transformation layer and persistence layer.
Figure 6. System architecture.
2.2.1 RDF transformation of caDSR and TCGA datasets
The XML2RDF tool, developed by the Redefer project (21), was used to transform
the XML based TCGA data dictionary and caDSR production CDEs into a corre-
sponding RDF representation. We loaded the resulting RDF datasets into a 4store
instance (CITE), an open-source RDF triple-store and exposed them via a SPARQL
endpoint, allowing us to use the SPARQL query language to preform semantic que-
ries across the datasets.
2.2.2 OWL-based schema for CIMI Reference Model and ISO 11179
We used the latest version of CIMI RM (v2.0.1) in XMI format. We then converted
the CIMI RM from XMI to RDF format using the Redefer XML2RDF transformation
services (21). We then defined the SPARQL queries to retrieve the UML based ele-
ments of the CIMI RM such as classes, attributes and associations. We created a
JAVA program that produces an OWL rendering of the CIMI RM using the
UML2OWL mappings specified by the Object Management Group (OMG) Ontology
Definition meta-model (ODM) standard (22). We finally harmonized and created an
OWL-based schema for CIMI RM and ISO11179.
2.2.3 Defining and populating reusable archetype patterns
We defined reusable archetype patterns that capture the clinical cancer domains de-
fined in TCGA data dictionary, their associated CDEs and the metadata structures
(Object Class, Property, Value Domain, etc.) recorded in the caDSR data repository.
We then defined a collection of SPARQL queries to retrieve the metadata elements
from both TCGA data dictionary and caDSR CDE dataset. Figure 7 shows a
SPARQL query example that retrieves all CDEs of the domain “clinical pharmaceuti-
cal” defined in TCGA data dictionary and their metadata recorded in caDSR CDE
dataset. We also developed a JAVA program that populates all reusable archetype
patterns in TCGA clinical cancer domains into the instance data against OWL-based
schema as we created.
Figure 7. A SPARQL query example that retrieves all CDEs of the domain “clinical
pharmaceutical” defined in TCGA data dictionary and their metadata recorded in
caDSR CDE dataset.
2.2.4 Evaluation of clinical utility
We finally performed a case study for the domain Clinical Pharmaceutical to demon-
strate clinical utility of our approach. Specifically, we demonstrated how many prop-
erties and enumerated value domains are enriched for the domain through the ISO
11179-based data elements recorded in the NCI caDSR. We then evaluate clinical
utility of the enriched data elements using a Medication template defined in CDISC
Clinical Data Acquisition Standards Harmonization (CDASH) standard (23).
3 Results
In total, TCGA data dictionary contains 38 clinical cancer domains and 775 CDEs,
which covers 21 cancer types. Table 1 shows a list of examples showing the clinical
cancer domains and the number of CDEs in each domain.
Table 1. A list of examples showing TCGA clinical cancer study domains and the
number of CDEs in each domain.
Number of
Clinical Cancer Domains CDEs Notes
clinical shared 98
clinical laml 49 Acute Myeloid Leukemia [LAML]
Cervical squamous cell carcinoma and endocervical
clinical cesc 47 adenocarcinoma [CESC]
clinical lgg 33 Brain Lower Grade Glioma [LGG]
clinical lihc 31 Liver hepatocellular carcinoma [LIHC]
clinical prad 25 Prostate adenocarcinoma [PRAD]
clinical paad 23 Pancreatic adenocarcinoma [PAAD]
clinical thca 20 Thyroid carcinoma [THCA]
clinical shared stage 19
clinical pharmaceutical 18
We created an OWL rendering of CIMI RM and harmonized it with the ISO 11179
metadata model schema, in which all classes defined in the CIMI RM are asserted as
the subclasses of an ISO 11179 class mms:AdministeredItem. Figure 8 shows a
screenshot of Protégé 4 environment illustrating the class hierarchy of OWL-based
schema for harmonized CIMI RM with ISO 11179 model.
Figure 8. A screenshot of Protégé 4 environment showing the class hierarchy of
OWL-based schema for harmonized CIMI RM with ISO 11179 model.
We populated reusable archetype patterns against the OWL-based schema and pro-
duced a metadata repository based in RDF format. The repository covers all 38 clini-
cal cancer study domains, comprising 316 distinct object classes, 4719 distinct prop-
erties, 1015 non-enumerated value domains and 1795 enumerated value domains (i.e.,
value sets).
Table 2 shows two pattern examples extracted from the TCGA domain “clinical
pharmaceutical”. Pattern 1 captures a number of CDEs asserted in the TCGA data
dictionary; Pattern 2 captures equivalent metadata structures (Object Class, Property,
Value Domain, etc.) recorded in the caDSR data repository. The 7 CDEs captured in
Pattern 1 have their “Object Class” in common that is “Pharmacologic Substance.”
The “Pharmacologic Substance” is linked with three “Property” instances: “Begin
Occurrence,” “End Occurrence” and “Continue Occurrence.” The properties are as-
sociated with 4 Value Domains: “Event Year Number”, “Event Month Number,”
“Event Day Number”, and “Yes No Character Indicator”.
Table 2. Two pattern examples extracted from the TCGA domain “clinical pharma-
ceutical”.
Pattern 1 Pattern 2
clinical pharmaceutical [ITEM_GROUP] clinical pharmaceutical [ITEM_GROUP]
Pharmacologic Substance [ITEM_GROUP]
Year Of Drug Therapy Start [ELEMENT] Begin Occurrence [ITEM_GROUP]
Month Of Drug Therapy Start [ELEMENT] Event Year Number [ELEMENT]
Day Of Drug Therapy Start [ELEMENT] Event Month Number [ELEMENT]
Event Day Number [ELEMENT]
Year Of Drug Therapy End [ELEMENT] End Occurrence [ITEM_GROUP]
Month Of Drug Therapy End [ELEMENT] Event Year Number [ELEMENT]
Day Of Drug Therapy End [ELEMENT] Event Month Number [ELEMENT]
Event Day Number [ELEMENT]
Therapy Ongoing [ELEMENT] Continue Occurrence [ITEM_GROUP]
Yes No Character Indicator [ELEMENT]
Evaluation results
As a case study, we looked into the domain Clinical Pharmaceutical that contains 18
CDEs. We retrieved the object classes recorded in caDSR and identified 11 distinct
object classes. And then, we retrieved globally in the caSDR CDE datasets for all
properties and value domains associated with the 11 object classes. Figure 9 shows a
bar graph illustrating the enrichment for the domain Clinical Pharmaceutical by data
element, property, value domain and enumerated value domain. The graph indicated
that the domain is greatly enriched with properties and value domains associated with
those 11 object classes, which forms a pool of data elements that could be used to
build detailed clinical models in this domain.
500 467
450
400
350
300 255
231
250
200
150 124
100
50 18 13 13 6
0
Data Element Property Value Domain Enumerated
Value Domain
Before enrichment After enrichment
Figure 9. A bar graph showing the enrichment for the domain Clinical Pharmaceutical
by data element, property, value domain and enumerated value domain.
To evaluate clinical utility of our approach, we aligned the data elements between
CDASH Medication and TCGA Clinical Pharmaceutical. Table 3 shows the align-
ment results. Out of 20 CDASH data elements with their data collection questions, 9
of them aligned with the CDEs asserted in TCGA data dictionary whereas 10 of them
aligned with those enriched data elements identified from our system. We believe that
the results demonstrated the enriched data elements are useful in building a clinical
model for the use cases beyond original TCGA data dictionary.
Table 3. Alignment results of data elements between CDASH Medication and TCGA
Clinical Pharmaceutical. Bold italic font indicates an enriched data element.
Prompt Data Element TCGA CDEs or Enriched Data
Question Text Name Elements
Were any medications taken? Any meds Administered
What is the medication/treatment
identifier? CM number Identifier; Unique Identifier
What was the term for the medica-
tion/therapy taken? Medication or Therapy Drug Name
Cytokine Administered; Placebo
Did the subject take ? tion/treatment> HER2/neu Administered
What were the active ingredients? Active Ingredients PubChem Compound Identifier
For what indication was the medi-
cation/therapy taken? Indication Indication
What was the ID for the adverse
events(s) for which the medication
was taken? AE ID Toxicity Description; Toxicity Grade
What was the ID of the medical MH ID
history condition(s) for which the
medication was taken?
What was the individual dose of
the medical/therapy? Dose Prescribed Dose
What was the total daily dose of
the medication therapy? Total Daily Dose Cumulative Agent Total Dose
What was the unit of the medi- Total Dose Units;Prescribed Dose
cal/therapy? Dose Unit Units
What was the dose form of the
medication/therapy? Dose Form Pharmaceutical Dosage Form Code
What was the frequency of the
medication/therapy? Frequency Number Cycles
What was the route of administra-
tion of the medication/therapy? Route Route Of Administration
Year Of Drug Therapy Start;Month Of
What was the start date of the Drug Therapy Start;Day Of Drug
medication/therapy? Start Date Therapy Start
What was the start time of the
medication/therapy? Start Time Agent Administered Begin Time
Was the medication/therapy taken
prior to the study? Taken Prior to Study? Prior Therapy Treatment Regimen
Year Of Drug Therapy End; Month Of
What was the end date of the Drug Therapy End; Day Of Drug
medication/therapy? End Date Therapy End
What was the end time of the
medication/therapy? End Time Agent Administered End Time
Is the medication/therapy still
ongoing? Ongoing Therapy Ongoing
4 Discussions
The metadata repository system proposed in this study has the following three major
implications. The first implication is that the system would enable producing a profile
of CIMI-compliant DCM models for TCGA clinical cancer study domains by leverag-
ing the best practice of DCM modeling in CIMI community. Pattern 1 as shown in
Table 2 is designed to capture a preferred set of CDEs and metadata for each domain
asserted in the TCGA data dictionary. The semantics captured in Pattern 1 should be
equivalent to those asserted in TCGA XML Schemas. In other words, Pattern 1 serves
as the CIMI-compliant representation of a preferred set of CDEs in a TCGA cancer
study domain.
The second implication is that we gained new insights on how ISO 11179 standard
could interact with CIMI RM for supporting detailed clinical modeling. The added
value would ultimately be the ability to represent ISO 11179 based constructs as con-
straints on CIMI RM. Pattern 2 is designed to capture equivalent metadata structures
(Object Class, Property, Value Domain, etc.) of a CDE informed by ISO 11179. As
shown in Table 2, Pattern 2 is represented in a post-coordination manner following
certain rules. The approach used in Pattern 2 is similar to the dissection approach that
is a common practice used in the terminology space for development of re-usable
terminologies. The dissection approach was originally used by the GALEN project
(24). In fact, the components in the metadata structure are usually annotated with
concept codes from a standard terminology. In NCI caDSR, NCI Thesaurus has been
largely used for the annotation purpose. Taking a look at Pattern 2 as shown in Table
2, “Pharmacologic Substance”, an object class, has NCIt code C1909 annotated;
“Begin Occurrence”, a property, has NCI codes “C25431:C25275” annotated. In addi-
tion, the post-coordination-based approach enabled us to globally retrieve all proper-
ties associated with a particular object class. For example, there are globally 40 prop-
erties associated with the object class “Pharmacologic Substance” in NCI caDSR,
resulting in additional 37 more properties and 5 more associated value domains. Fig-
ure 9 also shows such enrichment for the domain Clinical Pharmaceutical. We believe
that our approach would produce a rich collection of archetype patterns and con-
straints (e.g., datatypes, value sets, terminology bindings, etc.) that could be used to
facilitate detailed clinical modeling in clinical cancer study domain for use cases be-
yond TCGA.
The third implication is that we demonstrated the value of using Semantic Web tech-
nologies and tools in building such metadata repository. First, we created an OWL
rendering of CIMI RM. This allowed us to seamlessly integrate the CIMI RM with an
existing OWL-based ISO 11179 model. We envision that CIMI RM and ISO 11179
are two complementary standards that could greatly enhance the DCM modeling and
its metadata management. Second, we used XML2RDF Transformation technology to
transform XML-based TCGA data dictionary and caDSR CDE dataset into RDF-
based format. This allows us to use standard SPARQL query language to define que-
ries to retrieve metadata of a CDE across datasets while this enables a high-
throughput approach for globally searching metadata of nearly 50,000 CDEs recorded
in the NCI caDSR. Third, we populated reusable archetype patterns against the OWL-
based schema using a RDF-based representation. This will allow us to leverage the
built-in OWL DL reasoning capability and the RDF validation tools such as Shape
Expressions (25) to check the consistency and data quality of CIMI-compliant DCM
models.
5 Conclusion
In summary, we developed a use case-driven approach that enables a Semantic Web-
based metadata repository in support of authoring DCMs in clinical cancer study do-
mains. All schemas and datasets produced in this study can be accessible publicly at:
http://informatics.mayo.edu/caCDE-QA/index.php/Download. Future work will in-
clude 1) developing Semantic Web-based RESTful services for the archetype patterns
recorded in the metadata repository; 2) building quality assurance mechanism for
CIMI-compliant DCMs leveraging OWL DL reasoning and RDF validation tools; 3)
creating DCM authoring tools using the metadata repository as the backend; 4) devel-
oping tools that enable the transformation of DCM models between RDF/OWL-based
format and ADL-based format.
Acknowledgements: The study is supported in part by a NCI U01 Project – caCDE-
QA (1U01CA180940-01A1). The authors would like to thank Julie Evans and Dr.
Rebecca Kush from CDISC, for their kindly support and input.
6 References
1) Coyle JF, Mori AR, Huff SM. Standards for detailed clinical models as the basis
for medical data exchange and decision support. International journal of medical
informatics. 2003;69(2-3):157-74. Epub 2003/06/18. PubMed PMID: 12810120.
2) HL7 Detailed Clinical Models 2014 [September 10, 2014]. Available from:
http://wiki.hl7.org/index.php?title=Detailed_Clinical_Models.
3) Beale T. Archetypes and the EHR. Studies in health technology and informatics.
2003;96:238-44. Epub 2004/04/06. PubMed PMID: 15061551.
4) Clinical Element Model 2014 [September 10, 2014]. Available from:
http://www.clinicalelement.com/.
5) van der Kooij J, Goossen WT, Goossen-Baremans AT, Plaisier N. Evaluation of
documents that integrate knowledge, terminology and information models.
Studies in health technology and informatics. 2006;122:519-22. Epub
2006/11/15. PubMed PMID: 17102312.
6) Clinical Information Modeling Initiative (CIMI) 2014 [September 10, 2014].
Available from: http://informatics.mayo.edu/CIMI/index.php/Main_Page.
7) Chute CG, Pathak J, Savova GK, Bailey KR, Schor MI, Hart LA, et al. The
SHARPn project on secondary use of Electronic Medical Record data: progress,
plans, and possibilities. AMIA Annual Symposium proceedings / AMIA
Symposium AMIA Symposium. 2011;2011:248-56. Epub 2011/12/24. PubMed
PMID: 22195076; PubMed Central PMCID: PMC3243296.
8) Pathak J, Bailey KR, Beebe CE, Bethard S, Carrell DC, Chen PJ, et al.
Normalization and standardization of electronic health records for high-
throughput phenotyping: the SHARPn consortium. Journal of the American
Medical Informatics Association : JAMIA. 2013;20(e2):e341-8. Epub
2013/11/06. doi: 10.1136/amiajnl-2013-001939. PubMed PMID: 24190931;
PubMed Central PMCID: PMC3861933.
9) CDISC SHARE Project 2014 [September 10, 2014]. Available from:
http://www.cdisc.org/cdisc-share.
10) Jiang G, Evans J, Endle CM, Solbrig HR, Chute CG, editors. Using Semantic
Web Technologies for the Generation of Domain Templates to Support Clinical
Study Metadata Standards. SWAT4LS 2013 --Semantic Web Applications and
Tools for Life Sciences; 2013 December 10, 2013; Edinburgh, UK: CEUR
Workshop Proceedings; 2013.
11) Covitz PA, Hartel F, Schaefer C, De Coronado S, Fragoso G, Sahni H, et al.
caCORE: a common infrastructure for cancer informatics. Bioinformatics.
2003;19(18):2404-12. Epub 2003/12/12. PubMed PMID: 14668224.
12) ISO 11179 Specification [September 10, 2014].
http://standards.iso.org/ittf/PubliclyAvailableStandards/c050340_ISO_IEC_1117
9-3_2013.zip
13) Warzel DB, Andonaydis C, McCurry B, Chilukuri R, Ishmukhamedov S, Covitz
P. Common data element (CDE) management and deployment in clinical trials.
AMIA Annual Symposium proceedings / AMIA Symposium AMIA
Symposium. 2003:1048. Epub 2004/01/20. PubMed PMID: 14728551; PubMed
Central PMCID: PMC1480162.
14) TCGA BCR Data Dictionary 2014 [September 10, 2014]. Available from:
https://tcga-data.nci.nih.gov/docs/dictionary/.
15) TCGA Clinical Data 2014 [September 10, 2014]. Available from:
https://wiki.nci.nih.gov/display/TCGA/Clinical+data.
16) XMDR Project [September 10, 2014]. http://en.wikipedia.org/wiki/XMDR
17) Semantic MDR Project [September 10, 2014].
https://github.com/srdc/semanticMDR
18) CDISC2RDF Project [September 10, 2014]. https://github.com/phuse-
org/rdf.cdisc.org .
19) Body Temperature Archetype in ADL. [September 10, 2014].
https://github.com/opencimi/archetypes/blob/master/miniCIMI/CIMI-CORE-
ITEM_GROUP.body_temperature.v1.0.0.adls.
20) caDSR Downloads [September 10, 2014].
https://wiki.nci.nih.gov/display/caDSR/caDSR+Downloads.
21) Redefer Project [September 10, 2014]. http://rhizomik.net/html/redefer/.
22) OMG ODM Specification [September 10, 2014].
http://www.omg.org/spec/ODM/.
23) CDISC CDASH [September 28, 2014]. http://www.cdisc.org/cdash.
24) Rector AL, Rogers JE, Zanstra PE, Van Der Haring E; Open-
GALEN.OpenGALEN: open source medical terminology and tools. AMIA Annu
Symp Proc. 2003:982.
25) Shape Expressions [September 10, 2014]. http://www.w3.org/2013/ShEx/Primer.