=Paper=
{{Paper
|id=Vol-2969/paper14-DEMO
|storemode=property
|title=Reuse of Design Pattern Measurements for Health Data
|pdfUrl=https://ceur-ws.org/Vol-2969/paper14-DEMO.pdf
|volume=Vol-2969
|authors=Núria Queralt-Rosinach,Mark Wilkinson,Rajaram Kaliyaperumal,César Bernabé,Qinqin Long,Michel Dumontier,Paul Schofield,Marco Roos
|dblpUrl=https://dblp.org/rec/conf/jowo/Queralt-Rosinach21
}}
==Reuse of Design Pattern Measurements for Health Data==
https://ceur-ws.org/Vol-2969/paper14-DEMO.pdf
Reuse of Design Pattern Measurements for Health
Data
Núria Queralt-Rosinach1 , Mark Wilkinson2 , Rajaram Kaliyaperumal1 ,
César H. Bernabé1 , Qinqin Long1 , Michel Dumontier3 , Paul N. Schofield4 and
Marco Roos1
1
Leiden University Medical Center, Einthovenweg 20, 2333 ZC Leiden, The Netherlands
2
Universidad Politécnica de Madrid, Campus de Montegancedo, 28223 Pozuelo de Alarcón, Madrid, Spain
3
Institute of Data Science, Paul-Henri Spaaklaan 1, Maastricht University, Maastricht 6229EN, The Netherlands
4
University of Cambridge, Downing Street, Cambridge CB2 3DY, United Kingdom
Abstract
Research using health data is challenged by its heterogeneous nature, description and storage. The
COVID-19 outbreak made clear that rapid analysis of observations such as clinical measurements across
a large number of healthcare providers can have enormous health benefits. This has brought into focus
the need for a common model of quantitative health data that enables data exchange and federated com-
putational analysis. The application of ontologies, Semantic Web technologies and the FAIR principles
is an approach used by different life science research projects, such as the European Joint Programme
on Rare Diseases, to make data and metadata machine readable and thereby reduce the barriers for data
sharing and analytics and harness health data for discovery. Here, we show the reuse of a pattern for
measurements to model diverse health data, to demonstrate and raise visibility of the usefulness of this
pattern for biomedical research.
Keywords
Health data, Design pattern, Ontology, FAIR
1. Motivation
To enable informed healthcare decisions, hospitalised patients are characterised by different
health data such as travel history, comorbidities, and medications, and are monitored by clinical
measurements. Observational measurements provide insights into disease which range from
diagnosis and prognosis for individual patients to epidemiological understanding of the disease
in a population. The COVID-19 outbreak made clear that rapid analysis of observations across
a large number of healthcare providers can have enormous health benefits. This has brought
into focus the need for a common model of quantitative health data that enables data exchange
and federated computational analysis.
During the last virtual BioHackathon 2020 COVID-19, we created a minimal formal model
for COVID clinical observations using Semantic Web standards for quantitative traits, based
on quantitative information in the COVID-19 WHO RAPID Case Report Form. The model
FOIS 2021 Demonstrations, held at FOIS 2021 - 12th International Conference on Formal Ontology in Information
Systems, September 13-17, 2021, Bolzano, Italy
" n.queralt1_rosinach@lumc.nl (N. Queralt-Rosinach)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
describes clinical measurements to express quantities, their units, and the assay to obtain
the measurement [1]. The application of ontologies, Semantic Web technologies [2] and the
FAIR principles [3] is an approach used by different life science research projects, such as the
European Joint Programme on Rare Diseases (EJP RD) 1 , to make data and metadata machine
readable and thereby reduce the barriers for data sharing and analytics and harness health
data for discovery. Here, we show the reuse of the same design pattern for measurements to
model health data for three different applications: 1) observations in patient registries; 2) lab
measurements in hospitals; and 3) epidemiological measures in outbreaks.
2. The SIO Design Pattern Measurements
The Semanticscience Integrated Ontology (SIO) is an upper-middle level ontology that is com-
monly used to represent biomedical Linked Data [4]. SIO is an OWL ontology that provides
a simple, integrated ontology of types and relations for rich description of objects, processes,
and their attributes. It follows a worldview that primarily differentiates objects from processes:
objects are entities that occupy space (in their mass or energy), persist in time, and maintain
their identity even as they gain or lose parts. It also provides different Design Patterns (DP)
such as the DP Measurements, which overlaps with our minimal data model for quantitative
traits. The SIO DP Measurements [5] is a process-centric pattern that essentially relies on three
concepts: entity, quantity and measuring process (Figure 1). Quantities have specific values
that should be specified using the ’has value’ datatype property and the datatype. Units can
be specified using the Unit Ontology with the ’has unit’ object property. Quantities are the
result, i.e. the output of a measurement process and can be time-indexed to a time point or time
interval. The measuring process specifies that the output of the process is the creation of a
quantity. Entities can be described in terms of their quantified attributes. SIO also enables us to
specify which qualities, capabilities or roles are involved in a particular process, so as to more
richly describe the key components for that process to occur.
3. Applications
We reuse the SIO DP Measurements for three different applications, to represent:
3.1. Observations in Patient Registries
We apply the SIO DP Measurements to model patient observational health data. Patient registries
are organized systems that use observational methods to collect data, including longitudinal
data, on a population defined by a particular disease, condition, or exposure. In the Rare Disease
(RD) domain, they constitute key tools to pool data to achieve a sufficient sample size for
epidemiological and/or clinical research. The EJP RD is building a FAIR federated ecosystem to
enable efficient RD research. To increase interoperability among the enormously fragmentated
data from RD patients contained in hundreds of registries across Europe, the EJP RD dedicates
effort to build semantic data models for a set of common data elements defined for RD patient
1
European Joint Programme Rare Diseases (EJP RD) https://www.ejprarediseases.org/
Figure 1: The SIO design pattern for measurements.
registries by the European Joint Research Centre2 . The SIO DP Measurements pattern is reused
to provide the core foundation to build these semantic models to uniformly represent the
observations collected in patient registries [6] (see the EJP RD core model for these semantic
models, which is based on the SIO design pattern, in Figure 2). The modelling objective is to
represent every observation as the result of some measurement process with patients, clinicians,
and machines as participants. Application of the model will facilitate efficient, automated use
of registries to identify new pathways for treatment, develop clinical research tools, and recruit
potential participants for clinical trials.
2
https://eu-rd-platform.jrc.ec.europa.eu/set-of-common-data-elements_en
Figure 2: The EJP RD core model based on the SIO design pattern.
3.2. Laboratory Measurements in Hospitals
We apply the SIO DP Measurements to model patient quantitative health data. The worldwide
COVID-19 pandemic stressed the need to have patient data available and accessible for gaining
new insights timely and efficiently, not only within the hospital, but also across hospitals
and countries. Clinicians monitor biomolecular concentrations, other physiological signs, and
symptoms manifested in different organ systems of their patients at different points in time
and collect multi-omics data that need to be integrated for computational analysis. These
lab measurements are very valuable data because they give intrinsic information about the
underlying biological mechanism and patient disease trajectory that could be used to make
informed and tailored therapeutic decisions. The life science community has been developing
different ontologies to represent molecular biology, clinical measures, and disease phenotypes.
Based on the SIO DP Measurements and the EJP RD core model we are establishing an ontological
linking model of heterogeneous data such as immunoresponse-related lab measurements [7]
using OWL ontologies from the Open Biological and Biomedical Ontologies (OBO) Foundry,
SIO, and other Semantic Web standards with the aim of making clinical data amenable for
analysis with Linked Open Data and further ‘ontologised’ Linked Data from other hospitals.
3.2.1. Integration into GA4GH Phenopackets Standard
Phenopackets is an exchange standard for the description of aberrant phenotypes of human
subjects in relation to DNA sequence data, which is amenable for genomic research. Based on a
minimal overlapping model of the SIO DP Measurements, we implemented the ‘measurement’
Phenopackets extension in v2 to characterize clinical measurements3 .
3.3. Epidemiological Measures in Outbreaks
We apply the SIO DP Measurement to model quantitative epidemiological data. One year
ago, the novel COVID-19 infectious disease emerged and spread, causing high mortality and
morbidity rates worldwide. In the OBO Foundry [8], there are more than one hundred ontologies
to share and analyse large-scale datasets for biological and biomedical sciences. However, this
pandemic revealed that we lack tools for an efficient and timely exchange of this epidemiological
data which is necessary to assess the impact of disease outbreaks, the efficacy of mitigating
interventions and to provide a rapid response. In this work we reused the SIO DP Measurements
to develop an OBO ontology [9]. We aligned the SIO DP Measurements to the OBO principles,
and mapped classes and relations to OBO ontologies’ terms [? 10]. With the development of this
OBO ontology we provide a compatible logical model for quantities that enables researchers to
represent and share machine readable epidemiology surveillance data that can interoperate with
other biomedical ontologies in the OBO Foundry for rapid analysis, modelling and response.
4. Demonstration
We used the SIO DP Measurements to develop ontological models amenable for analysis and
the development of computer applications, such as semantic similarity, semantic mining, ma-
chine learning or feature embedding, reasoning and biomedical predictions. In this dynamic
demonstration, we will show how to design semantic models using the SIO DP Measurements
to represent three different health data sets. The aim is to make attendees gain understanding
of the rationale underlying this SIO design pattern. Therefore, we will model some instances
together, such as observations in patient registries, lab measurements, and epidemiological
variables.
5. Discussion and Conclusion
Data harmonization based on DP enables efficient research. For example, it allows querying of
heterogeneous data that were modelled using the same pattern. In Semantic Web applications,
this feature is an opportunity to build SPARQL queries with a simple canonical graph pattern,
thus not only improving interoperability of FAIR data, but also reusability. Furthermore, this har-
monized representation of data at patient and population levels may also bring the opportunity
3
https://phenopacket-schema.readthedocs.io/en/v2/measurement.html
to design an axiom pattern to link epidemiological data with additional clinical data. This may
help to represent computable cohorts for precision medicine and raise the exciting opportunity
to apply formal reasoning for knowledge discovery. While there are several ontologies and
design patterns that capture measurements and are applied in similar contexts, e.g. LOINC 4
in clinical contexts, the Clinical Measurement Ontology 5 in some model organisms and a
schema for the description of phenotypes [11], here we demonstrated that reusing the same
design pattern for measurements can represent heterogeneous health data and can be applied
in diverse contexts from clinical measurements in hospitals to elements in patient registries
and measures in epidemiological studies for outbreak monitoring. Remaining challenges for
cross-institutional analysis are for instance preserving patient data-privacy and safety. However,
these challenges are not blockers for making data interoperable, i.e. they can be addressed in
parallel. In summary, the application of the SIO DP Measurements resulted in three diverse
biomedical applications: 1) the semantic harmonization of observational real world patient data;
2) the development of a semantic model for data integration within the hospital; and 3) the
development of an OBO ontology for monitoring outbreaks. With the demonstration of the SIO
DP Measurement, we aim to raise visibility and foster understanding on how to use it for health
data modelling and integration. Future steps are the application of building ontology-based
knowledge graphs and exploit harmonized patient data by federated query and analysis.
Acknowledgments
This initiative is supported by funding from the European Union’s Horizon 2020 research and
innovation program under the EJP RD COFUND-EJP N° 825575. We would also like to thank to
the EJP RD, the GO FAIR VODAN, and the ZonMW Health Holland under the Trusted World
of Corona, for supporting the research on FAIR data that was reused here. We would like to
acknowledge that work in the BEAT-COVID project was partly funded by the Wake Up To
Corona crowdfunding initiated by the Leiden University Fund (LUF).
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