=Paper=
{{Paper
|id=Vol-1795/paper26
|storemode=property
|title=Case Report Form based on semantic Web technologies
|pdfUrl=https://ceur-ws.org/Vol-1795/paper26.pdf
|volume=Vol-1795
|authors=Angel Esteban-Gil,Jesualdo Tomás Fernández-Breis
|dblpUrl=https://dblp.org/rec/conf/swat4ls/Esteban-GilF16
}}
==Case Report Form based on semantic Web technologies==
https://ceur-ws.org/Vol-1795/paper26.pdf
Case Report Form based on semantic Web technologies
Ángel Esteban-Gil1 and J. T. Fernández-Breis2*
1
Fundación para la Formación e Investigación Sanitarias de la Región de Murcia, IMIB-
Arrixaca-UMU, 30003 Murcia, Spain
angel.esteban@ffis.es
2
Dpto. Informática y Sistemas, Facultad de Informática, Universidad de Murcia, IMIB-
Arrixaca-UMU, 30100 Murcia, Spain
jfernand@um.es
Abstract.OBJECTIVE: Improving the capture, sharing and reuse of clinical re-
search data within a biomedical research institute through the use of semantic
case report forms (CRF).
BACKGROUND:Biomedical researchers need software solutions that allow work-
ing in projects with different, heterogeneous and changing information. A CRF
is a set of questionnaires used for capturing the data of the patients recruited in
a biomedical research study. Current CRF technological solutions have little
flexibility to modify their structure to adapt to new requirements without major
software changes, and they lack a well-defined model for the exploitation, gen-
eration of alerts or data quality assurance.
METHODS: Our approach divides the CRF building in two phases: (1) the defini-
tion of the data structure and the workflow to register these data, and (2) the re-
cruitment process where the CRF captures the clinical information of each pa-
tient and the exploitation of the results of the biomedical project. OWL ontolo-
gies are employed for the formal CRF representation including the workflow of
the patients recruited in the biomedical project. RDF repositories were used to
store the questionnaire of each patient in every stage and SPARQL was used to
exploit the semantic information.
RESULTS: In this work we present a web platform that incorporates the benefits
of Semantic Web technologies to build, execute and exploit CRFs in biomedical
projects. Our platform contains data of more than 14.000 patients recruited in
more than 100 biomedical research projects running in our research institute.
CONCLUSION: Semantic Web technologies facilitate the construction of CRF
platforms that meet the needs of biomedical researchers. We plan to improve
the interoperability of the CRF data retrieval process by providing extracts
compatible with standards such as HL7, CEN/ISO 13606 or OpenEHR.
Keywords: Biomedical Informatics, Semantic Web, Case Report Form, Ontol-
ogy
1 Introduction
Biomedical researchers need software solutions able to exploit heterogeneous dy-
namic, project-specific information. A case report form (CRF) is a set of question-
naires used for capturing the data of each patient recruited in a biomedical research
project [1]. However, heterogeneity is common in CRFs, because each study defines
its own report schemas. More than 48000 clinical trials have been registered in Eu-
rope since 2014 [2], which means that such studies manage a large volume of infor-
mation.
The Semantic Web can be seen as an extension of the current web, in which infor-
mation is given well-defined meaning, better enabling computers and people to work
in cooperation [3]. Ontologies [4] constitute the standard knowledge representation
mechanism for the Semantic Web, and technologies such as OWL [5], RDF [6], and
SPARQL [7] enable a formal representation of the domain, the data and their exploi-
tation.
Many technological solutions are available to manage data for CRF nowadays [1],
which can be grouped in two classes according to the type of technology used for
representing and persisting the data: relational databases; and non-relational databas-
es. The main disadvantage of the first approach is the little flexibility of the relational
model for structural modifications without major changes in the software. The main
disadvantage of the second approach is the lack of a well-defined model for exploita-
tion, generation of alerts, or quality assurance of the data.
Our main objective is to develop a Web platform that facilitates the process of
building and managing a CRF using Semantic Web technologies including: (1) the
use of Semantic Case Reports Form for capturing the clinical data, and (2) the defini-
tion of customizable search interfaces and dashboards for the analysis and visualiza-
tion of patient data.
In this context, our approach uses semantic web technologies for storing biomedi-
cal data in a flexible data model and exploiting it thanks to the semantic model that
describes the data. Furthermore, these technologies permit the reuse of biomedical
ontologies and the semantic interoperability of health resources when are required.
Our approach has been applied so far in 10 biomedical projects and in 91 clinical
trials, whose samples are stored the biobank in the Institute for Bio-health Research of
Murcia (IMIB-Arrixaca-UMU) in Spain.
2 Methods
Our approach has two main stages (see Figure 1). The first stage is the definition of
the data structures and the workflow that will be used for data capture. Workflows
permit to determine the data capture stages included in the clinical study. The second
stage is the execution of the CRF, which consists of capturing and exploiting the pa-
tient data.
One special feature of our approach is that the data manager can change the data
structures and data workflow during the CRF execution. This feature provides flex-
ibility
ity to biomedical researchers,
researchers who can adapt their datasets to new requirements.
This is enabled by the use of an OWL ontology1, which can be extended with the
specific concepts of the projects. This ontology has been built based on the existing
ontologies like OBI[8]
[8], SIO[9] and SOPHARM[10]. The basic sic concepts of this ontol-
onto
ogy are described next::
• Stakeholder. The recruitment process of a biomedical study has several stakehold-
stakehol
ers: (1) the Datamanager represents the responsible of the patient data capture;
capture (2)
the Researcher stands for users who can capture and exploit the information, mation, (3)
the Monitor represents users who can monitor the captured patient data and the ad-
verse effects in the study;
study and (4) the Manager represents users who promote the
clinical study, can define the CRF and exploit its results.
res The manager is often the
promoter
ter or funder of the study, such as a pharmaceutical laboratory or a hospital.
• Project. Thee ontology includes a hierarchy of types of biomedical projects such as
Clinical Trials, Observational Studies, Cohort Studies, etc.
et
• Patient. For each patient recruited, and due
d to the Spanish data protection
otection law,
law we
only capture gender,
gender birth date and a code that ensures the anonymity of the pa-
tient. Each individual of the class Patient has a Protocol in the project. This per-
mits, for instance, sepa
eparating the sick individuals from the healthy ones.
• Report. This concept represents the set of information that must be captured
capt in a
concrete clinical interaction:
interaction: the applied therapy, the results of a medical test, etc.
• Stage. This conceptt represents the stage or phase of a patient. For For each stage the
data manager can capture one or more Reports.
Fig. 1. Methodology schema
A Semantic Case Report Form is defined as an instance of Report that contains the
answers
nswers to the items of the questionnaire, which are associated with a given Patient,
which is in a concrete stage of its disease.
2.1 CRF Definition
The CRF definition has two phases: (1) the definition of the reports and (2) the de-
d
finition
inition of the workflow for
fo each patient included in the biomedical project.
1
http://www.imib.es/ontologies/CRDv4.owl
The generation of reports consists of defining the data capture fields. Our approach
allows the definition of different types of fields: numbers, dates, times, text, boolean
and enumerated. Enumerated fields permit to select and reuse, as values, classes from
existing ontologies, including those used in other reports. All the fields and reports
can be reused in different stages of the protocol or in different studies allowing the
standardization of the information, so enabling its sharing and comparability.
When the data manager associates fields in a report, she can apply the following
types of rules, which are implemented in the semantic model:
• Cardinality rules. They indicate the minimum and maximum cardinality for a
given datum in this report. The cardinality can be a fixed value or it can be relative
to the values of other fields. For example, a field “number of children” may affect
the number of times age values for the children will have to be stored. Other exam-
ple could be a field with the question “Do you smoke?”. In the case of negative an-
swer the field “number of daily cigarettes” could be null.
• Range rules. They indicate the range of values for a field in a report.
• Format rules. They are regular expressions to satisfy by the user when providing a
value for this field. These rules are useful to store values such as emails, phone
numbers, etc.
Our approach also permits to define derived fields. For example, if we have a field
“weight” and other “height”, the data manager can create a new field named “body
mass index” calculated from the values of the previous fields.
The definition of workflows is based on state machines. In [11], the authors present
a set of initial and end states and a set of intermediate states where the information
transit from the initial state to the final one. When the data manager defines the states
of the study, each one has a state machine associated. The configuration of each state
requires the next information:
• Transitions between states. The transitions represent the path for the patient data
in the recruitment process of a biomedical project.
• People responsible of the data capture.
• Reports. Which reports have to be filled in each state. For example, the screening
phase of a clinical trial may include a report related to blood test, which will be
used for recommending the next state for the patient.
• Alerts.
─ Time alerts. The patient is in the same state longer than expected.
─ Data quality alerts. The captured data are not enough to characterize the clini-
cal features of the patient.
Figure 2 shows an example of a state machine. We can observe an initial state
(blue), several end states (red) and a set of intermediate states related by transitions
between them. Each state may have one or more responsible people, and have differ-
ent alerts and reports associated.
Fig. 2. An example of a general state machine used in our approach
Semantic transformation
The ontology that provides the basic knowledge entities is extended with entities of
interest for the CRF. We initially proposed to the biomedical researchers the use of
Protégé2 for such purpose, which was rejected by them. Hence, we developed a web
editor with features closer to their requirements and more intuitive for the intended
users of the system. This also required developing a process to transform the content
generated with this editor to OWL, what was done applying the following steps:
• Generation of the biomedical project and the different stakeholders that will com-
plete and review the recruitment process in the CRF.
• Generation of the several protocols for each patient in each biomedical project.
• Creation of classes for each defined report. Each report has the fields as
owl:DatatypeProperty and relationships as owl:ObjectProperty defined by the data
manager. Each field maintains the rules of integrity, cardinality and range.
• Generation of the workflow of the CRF using our model of state machine. Besides
the workflow generation, we need a class that represents the patient stage in each
phase of the study. Another important aspect is the generation of the rules to transit
between states, and the preconfigured alerts.
The ontology generated is stored in a document management system (DMS) with
version control. The DMS helps us to exploit information captured in older versions
of the recruitment protocol of the study. Our approach allows the user to choose the
ontology version to exploit the clinical information stored in RDF.
2.2 CRF Execution
The CRF definition produces an OWL ontology that represents the structure of the
data to be captured and the workflow to be applied to each patient recruited in the
clinical study. Starting the recruitment requires to use our semantic running engine to
2
http://protege.stanford.edu/
capture the data and our semantic exploitation model to take advantage of the infor-
mation registered in the CRF.
Semantic running engine
The semantic running engine generates web forms for adding and updating the in-
formation of each semantic report, applying the rules defined in the report fields and
in the state machines. The information is stored in a semantic repository with two
types of data sources: (1) an OWL files server with the formal representation of the
domains, and (2) an RDF repository which stores the data. We use Virtuoso3 as data
store. Virtuoso has been used in other effort such as [12].The ontologies guide all the
layers of the solution: data capture, information delivery and exploitation.
The ODS (Ontology Driven-Searcher)[13] is the service for information delivery.
This tool is an editor of SPARQL queries supported by OWL models. The tool uses
the underlying CRF ontologies to show the necessary information to visually define
SPARQL queries.
Semantic exploitation model
Our proposal includes a set of methods for exploiting the information stored in the
semantic repository:
• Semantic searcher. This method uses the ODS for defining queries over the se-
mantic CRF data model.
• Alert management. This method allows the generation of alerts over the semantic
data. It uses the ODS for defining the alerts as queries and comparing the results
with thresholds when these have been defined. For example, if the value of the sys-
tolic blood pressure is greater than 15, then the user may receive a high critical
alert that implies that this patient is not suitable for the clinical study.
• Semantic dashboard. This tool permits users to formulate incremental, user-
defined queries with a graphical user interface based on the ODS. The query results
can be displayed in several customizable ways, allowing for the generation of on-
demand dashboards.
3 Results
The approach described in the previous section has been applied in more than 100
biomedical projects in the Institute for Bio-health Research of Murcia (IMIB-
Arrixaca-UMU). The platform is completely functional since January 2015. Our plat-
form has had an important impact in our biomedical research institute. Nowadays we
have the next results:
3
http://virtuoso.openlinksw.com/dataspace/doc/dav/wiki/Main/
• More than 14.000 patients have been recruited in several studies.
• More than 9.500 reports have been registered in the platform.
• More than 70 reports have been defined with more than 1.500 fields.
• More than 300 stages have been defined for the biomedical projects.
• The researchers have reused only 41 fields between reports.
• The researchers have reused only 10 reports between clinical studies.
• The platform has more than 50 users.
• Two CRFs involve patients from several regions of Spain.
• The researchers have configured 6 dashboards to exploit the data in real time. Fig-
ure 3 illustrates how the researcher uses the ODS to define a query over the seman-
tic repository to represent graphically the blood type of the patients recruited in a
biomedical project. This variable is captured in the report called “CRD1M”.
• One biomedical project is using a standardized ontology, the ICD10 one[11].
Fig. 3. Semantic dashboard
Thanks to the feedback of the researchers of the biomedical groups, we have in-
cluded additional services to the platform:
• Generation of the patient’s visit calendar from the information of the workflow of
the clinical study. This calendar allows the generation of alerts for the clinical
when they have to contact the patient.
• Use of a web calculator to define derived fields. An example that calculates the
body mass index is shown in Figure 4.
• Use of the reports to characterize, not only patients, but also their biological sam-
ples, such as data from the pathological analysis of a tumor.
• Filling of the reports from mobile devices such as smartphones or tables.
• Generation of PDF reports with the results of the patients saved in the CRF.
Fig. 4. Definition of a derived field
4 Discussion and conclusions
In this work we have demonstrated that the semantic technologies are able to man-
age and exploit the results of the patients’ recruitment process in any clinical study.
We have evaluated this tool following the method proposed in [1], obtaining with
good results in comparison to other tools available in the market. Our platform fulfills
all criteria except the capability to export the data in CDISC format [15].
Many researchers have integrated semantic web technologies in biomedical re-
search. We have grouped these proposals in two types: (1) the use of ontologies to
classify the clinical information [16], and (2) enrichment of biomedical data for ex-
ploiting using semantic technologies [17]. Our approach is different because we re-
solve the problem from a global perspective, trying to use semantic technologies in
the whole life cycle of the biomedical project. The main advantage of our proposal is
that ontologies guide all the process of the biomedical project including the capture of
data and its exploitation. Furthermore, the logical schema of the CRF may help to
understand the recruitment process and the results of the study.
Our approach shows how semantic web technologies permit researchers to adapt
the CRF to their specific requirements without the help of and IT expert. The use of
an RDF repository allows for building a robust and scalable architecture for big clini-
cal data warehouses [12]. Furthermore, this architecture is very flexible in changing
environments as the biomedical research. The use of OWL ontologies to represent the
knowledge stored in the RDF repository allows to exploit it using technologies such
as the ODS to define queries without mastering semantic technologies. Another im-
portant benefit in the use of OWL is the capability to reuse the fields and concepts
among several projects and to take advantage of the clinical knowledge modeled in
this format.
We have learned from the use of the platform that some users are only interested in
the exploitation of data, so we are developing data retrieval methods for importing
data from other systems they are currently using to capture patient data.
Finally, this platform has also an economic impact in our organization. In the last 6
years, IMIB-Arrixaca-UMU has run 12 clinical trials funded by industry. Our re-
searchers have used paper-based CRF in six of them. The cost of the electronic CRF
used in the other six clinical trials was almost 55.000 € (9.000 € on average). We are
not able to calculate the effort to exploit the paper-based CRF data, but our electronic
CRF platform, which has been used in ten non-industrial clinical trials, has permitted
to save approximately 90.000 €.
Our approach presents some limitations: (1) our solution is not able to automatic
retrieve data from other clinical systems, (2) our solution does not implement any
clinical standard to interoperate with other clinical software, (3) we have not been
able to convince researchers to publish and share their questionnaires,(4) the re-
searchers still prefer CSV data exploitation instead of using our semantic exploitation
model, (5) our reuse of biomedical ontologies is still limited and (6) we are exploiting
OWL reasoning yet.
As future work we plan to improve the interoperability between our CRF and other
clinical systems implementing standards as HL7, CEN/ISO 13606 or openEHR. We
also plan to export the data to CDISC [15]. We are planning to incorporate ontology
alignment techniques to improve the reuse and standardization of our CRF semantic
models. Finally, we plan to provide training to promote the use of the semantic ex-
ploitation model.
To conclude, the construction of tools that facilitate the use of standard clinical
terminologies or the reuse of fields or reports will improve the exploitation of the data
aggregated of several patients included in different studies achieving the desired real
goal in the biomedical research: improving health care to the patient.
Acknowledgments
This project has been possible thanks to the cooperation of the clinical research
groups of the Institute for Bio-health Research of Murcia (IMIB-Arrixaca-UMU).
This work was supported by the Ministerio de Economía y Competitividad and the
FEDER programme through grant TIN2014-53749-C2-2-R2, and the Fundación
Séneca through grant 19371/PI/14.
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