=Paper=
{{Paper
|id=Vol-1747/BIT104_ICBO2016
|storemode=property
|title=Cardiovascular Health and Physical Activity: A Model for Health Promotion and Decision Support Ontologies
|pdfUrl=https://ceur-ws.org/Vol-1747/BIT104_ICBO2016.pdf
|volume=Vol-1747
|authors=Vimala Ponna,Aaron Baer,Matthew Lange
|dblpUrl=https://dblp.org/rec/conf/icbo/PonnaBL16
}}
==Cardiovascular Health and Physical Activity: A Model for Health Promotion and Decision Support Ontologies ==
https://ceur-ws.org/Vol-1747/BIT104_ICBO2016.pdf
Cardiovascular Health and Physical Activity:
A Model for Health Promotion and Decision Support Ontologies
Vimala Ponna Aaron Baer Matthew Lange, PhD
Department of Neurobiology, Department of Food Science and Department of Food Science and
Physiology, and Behavior Technology Technology
University of California at Davis University of California at Davis University of California at Davis
Davis, CA Davis, CA Davis, CA
vmponna@ucdavis.edu ambaer@ucdavis.edu mclange@ucdavis.edu
Abstract— Current cardiovascular disease decision support repository and scientific workflow capable of providing the
systems (DSS) rely primarily on ontologies that characterize and foundation for accelerated creation of health-focused
quantify disease, recommending appropriate pharmacotherapy ontologies. This semi-automated workflow enables conversion
(PT) and/or surgical interventions (SI). PubMed and Google of textual annotations from scientific literature into triples
Scholar searches reveal no specific ontologies or literature
(knowledge propositions in the form of semantic triples). A
related to DSS for recommending physical activity (PA) and diet
interventions (DI) for cardiovascular health and fitness (CVHF) semantically enabled backend repository stores triples
improvement. This dearth of CVHF-PA/DI structured combined from many sources. Knowledge gleaned from
knowledge repositories has resulted in a scarcity of user-friendly multiple, sometimes-conflicting sources enables these triples
tools for scientifically validated information retrieval about from many sources of literature into one conceptual map with
CVHF improvement. Advancement of health science depends on visualization of new and unexpected relationships in the form
timely development and implementation of health (rather than of a conceptual lattice. With the help of Protégé, these
disease) ontologies. We developed a time-efficient workflow for conceptual lattices convert to health-focused ontologies,
constructing/maintaining structured knowledge repositories which equip DSS with knowledge regarding PA and DI.
capable of providing informational underpinnings for CVHF-
PA/DI ontologies and DSS that support health promotion,
Ultimately, health-focused ontologies and DSS provide
including precise, personalized exercise prescription. This patients, physicians, and researchers easy access to knowledge
workflow creates conceptual lattices about effects of varied PA on health trajectories, health improvement, and individual
on CVHF. These conceptual maps lay the foundation for health outcomes. By employing this semi-automated
accelerated creation of health-focused ontologies, which workflow and enabling concept lattice to ontology conversion,
ultimately equip DSS with CVHF knowledge related PA and DI. we have created an express tool for health-focused data
extraction. With this system, modern medicine can embrace
the idea of health promotion, rather than disease risk
INTRODUCTION
assessment.
Current healthcare ontologies and DSS rely primarily on
knowledge relevant to disease risk assessment and treatment DESIGN AND METHODS
and are focused almost entirely on assessing PT and SI. We employed open source and commercial off the shelf
Analogous ontologies and DSS for advancing consumer health technologies including Zotero [1], Excel [2], MySQL [3],
via PA do not yet exist. Successful implementation of Python [4], Cmap [5], and Protégé [6] as part of the semi-
healthcare ontologies and DSS for recommending specific PT automated workflow for easy data mining and concept lattice
and SI for cardiovascular diseases are built upon databases extraction from literature. This workflow begins in Zotero’s
from clinical trials and patient records, combined with highly PDF viewer where human annotation takes place to highlight
curated, hierarchical vocabularies of diseases, diagnoses, PT, and note semantic triples of interest in an article as illustrated
and SI. PubMed and Google Scholar searches reveal no in Fig. 1. Next, the “extract annotations” tool in Zotero is used
scientific literature about healthcare ontologies and consumer to create a .txt file, shown in Fig. 2, of the annotations made.
DSS for CVHF using analogous systems related to knowledge The information in this .txt file is then transferred to Excel
about PA and DI for health improvement. Medicine today where a macro parses the annotations into four columns as
relies heavily on modeling disease, rather than modeling represented by Fig. 3. The .csv file created in Excel is then
health. Part of the problem is the dearth of queryable, curated imported into a table in MySQL and further parsed into a three-
column table shown in Fig 4. The table in MySQL is exported
and structured knowledge repositories dedicated to CVHF
as a .txt file and imported as “Propositions to text” in Cmap,
relative to specific DI and PAs. These immense reserves of
creating a concept map, part of which can be seen in Fig. 5.
information often require time-consuming data mining and Finally, the concept maps obtained from such articles can be
inhibit timely advancement of health and lifestyle science. exported as .cxl files, reformatted to .owl files, and imported
User-friendly tools for information retrieval from scientific into Protégé for ontology creation. As an example, we utilized
literature such as research articles, clinical studies, and this semi-automated workflow to extract information from
published texts have yet to be pioneered. We developed a “Potential adverse cardiovascular effects from excessive
straightforward, time effective structured knowledge endurance exercise” by O’Keefe et al. and create a conceptual
lattice about the effects of PAs with varied types, intensities,
durations and frequencies on CVHF [7]. A total of 177 unique
concepts, 49 linking phrases, and 156 propositions were
compiled from the article. These concepts are linked to
concepts in other maps created from ontologies, for example
The Foundational Model of Anatomy Ontology [8].
Fig. 5. Two concepts from article in CMAP.
Sustainability plans for the ontology will be developed once
we receive initial feedback from the community about how
paths forward for integration with related ontologies. We have
not yet tested this initial ontology.
CONCLUSIONS AND FURTHER RESEARCH
We have created a prototype platform for semi-automated
concept lattice generation from data mining that is easy to use,
integrates information, and creates visualization for a
knowledge network. It enables health professionals in
preventing health problems before they start, bringing an
Fig. 1. Article annotations in Zotero’s PDF viewer.
enormous change to the medical industry. Immediate
implications of this workflow are the creation of a health-
focused ontology for individuals who engage in vigorous
exercise and their physicians who may use it as a teaching
tool. The health-focused ontology built on PA can be
combined with the creation of other health-related ontologies
related to PA, DI, and other health improvement methods, as
part of a multi-ontology framework to accelerate the
development of health promotion [9]. Correlational
relationships discovered from integration of multiple
ontologies will provide foundations for more research on
health promotion. Further automation of this semi-automated
workflow will make health-focused ontology creation even
faster and more easy to use. Part of this automation process
will employ development of add-on functions within Zotero,
eliminating the use of Excel and extracting concepts directly
into the database. Additional steps would include crowd-
Fig. 2. Extracted annotations as .txt file using tool within Zotero. sourcing information by enabling this tool to communicate
through web services into cross-disciplinary conceptual
lattices. The goal is to develop an environment where, with
minimal oversight, one can move from textual annotations
into map creation easily. Ultimately, this will lay the
foundation for building a large repository of structured
knowledge related to PA and provide a model for mapping
other human behaviors to individual health outcomes.
However, in working with this prototype semi-automated
workflow, errors involving imprecise language and varying
Fig. 3. Extracted annotations parsed to four-column table in Excel. tense highlight the need for detailed inspection and refinement
of annotations. These errors emphasize areas of ambiguous
jargon used in health, which need to be explicitly
characterized. Such manual inspections take considerable time
and underscore the need for semi-automated concept/linking
phrase suggestion mechanisms. Despite its errors, this
prototype semi-automated workflow serves as the solution for
the dire necessity of a fast, accessible, and comprehensible
system for improving current knowledge and information
about health promotion in medicine.
Fig. 4. Extracted annotations parsed to three-column table in MySQL.
REFERENCES [5] Florida Institute for Human & Machine Cognition (2014). Cmap.
Retrieved from http://cmap.ihmc.us
[1] Roy Rosenweig Center for History and New Media. Zotero. Retrieved
[6] Stanford Center for Biomedical Informatics Research (2016). Protégé.
from https://www.zotero.org
Retrieved from http://protege.stanford.edu
[2] Microsoft Office (2016). Microsoft Excel. Retrieved from
https://www.microsoftstore.com/store/msusa/en_US/pdp/productID.323 [7] J. O’Keefe et al., “Potential adverse cardiovascular effects from
excessive endurance exercise.” Mayo Clinic Proceedings 87.6, pp. 587–
021400?s_kwcid=AL!4249!3!105984118253!e!!g!!excel&WT.mc_id=p
ointitsem+Google+Adwords+5+- 595, 2012.
+Excel+2016&invsrc=search&ef_id=UsDsQwAAAWWySTEQ:201607 [8] C. Rosse, J. Mejino, “A reference ontology for biomedical informatics:
01200844:s the foundational model of anatomy.” Journal of Biomedical Informatics
[3] Oracle Corporation and/or its affliliates (2016). MySQL. Retrieved from 36.6 pp. 478–500, 2003.
https://www.mysql.com [9] M. Lange, D. Lemay, J. German, “A multi-ontology framework to guide
agriculture and food towards diet and health.” Journal of the Science of
[4] Python Software Foundation (2016). Python. Retrieved from
https://www.python.org Food and Agriculture 87 pp. 1427-1434, 2007