=Paper=
{{Paper
|id=None
|storemode=property
|title=Browsing Causal Chains in a Disease Ontology
|pdfUrl=https://ceur-ws.org/Vol-914/paper_51.pdf
|volume=Vol-914
|dblpUrl=https://dblp.org/rec/conf/semweb/KozakiKYIOM12
}}
==Browsing Causal Chains in a Disease Ontology==
https://ceur-ws.org/Vol-914/paper_51.pdf
Browsing Causal Chains in a Disease Ontology
Kouji KOZAKI1, Hiroko KOU1, Yuki Yamagata1, Takeshi IMAI2, Kazuhiko OHE2
and Riichiro MIZOGUCHI
1
The Institute of Scientific and Industrial Research, Osaka University
8-1 Mihogaoka, Ibaraki, Osaka, 567-0047 Japan
2
Department of Medical Informatics, Graduate School of Medicine, The University of Tokyo,
7-3-1, Hongo, Bunkyo-ku, Tokyo, Japan
{miz, kozaki, kou, yamagata}@ei.sanken.osaka-u.ac.jp
{ken, kohe}@hcc.h.u-tokyo.ac.jp
Abstract. In order to realize sophisticated medical information systems, many
medical ontologies have been developed. We proposed a definition of disease
based on River Flow Model which captures a disease as a causal chain of clini-
cal disorders. We also developed a disease ontology based on the model. It in-
cludes definitions of more than 6,000 diseases with 17,000 causal relationships.
This demonstration summarizes the disease ontology and a browsing system for
causal chains defined in it.
Keywords: disease ontology, causal chain, ontology visualization
1 Introduction
In these days, medical information systems store huge amount of data. Semantic
technologies are expected to contribute to effective use of them, and many medical
ontologies such as OGMS [1], DOID [2], and IDO [3] have been developed for realiz-
ing sophisticated medical information systems. They mainly focus on the ontological
definition of disease with related properties. The authors proposed a definition of a
disease involving capturing a disease as a causal chain of clinical disorders and a
computational model called River Flow Model of Disease [4, 5]. Based on the model,
we developed a disease ontology which includes definitions of about 6,000 diseases
with causal relations between 17,000 clinical disorders (abnormal state). This demon-
stration shows a system to browse causal chains defined in the disease ontology.
This article is organized as follows. The next section overviews the River Flow
Model of Disease discussed in [4, 5]. Section 3 summarizes developments of the dis-
ease ontology and a browsing system for the disease ontology. Finally, we present
concluding remarks together with future work.
2 River flow model of disease
After it begins to exist, a typical disease, as a dependent continuant, enacts extending,
branching, and fading processes before it disappears. Thanks to these processes, a
disease can be identified as a continuant that is an enactor of those processes. Such an
entity (a disease) can change according to its phase while keeping its identity. On the
basis of this observation, we defined a disease as:
Definition 1: Disease [4]
A disease is a dependent continuant constituted of one or more causal chains of
clinical disorders appearing in a human body and initiated by at least one disorder.
Note that, although any disease has dynamic flows of the propagation of causality
as its internal processes, it is the enactor of its external processes, such as branching
and extending its causal chain of disorders. When we collect individual causal chains
belonging to a particular disease type (class), we are able to find a common causal
chain (partial chain) that appears in all of the instance chains. By generalizing such a
partial chain, we obtain the notion of a core causal chain of a disease as follows:
Definition 2: Core causal chain of a disease
A sub-chain of the causal chain of a disease whose instances are included in all the
individual chains of all instances of a particular disease type. It corresponds to the
essential property of a disease type.
Definition 2 provides a necessary and sufficient condition for determining the dis-
ease type to which a given causal chain of clinical disorders belongs. That is, when an
individual causal chain of clinical disorders includes instances of the core causal
chain of a particular disease type, it belongs to the disease type. We can thus define
such a disease type that includes all possible variations of physical chains of clinical
disorders observed for patients who contract the disease. According to a standard
definition of subsumption, we can introduce an is-a relation between diseases using
the chain-inclusion relationship between causal chains.
Definition 3: Is-a relation between diseases
Disease A is a supertype of disease B if the core causal chain of disease A is includ-
ed in that of disease B. The inclusion of nodes (clinical disorders) is judged by tak-
ing an is-a relation between the nodes into account, as well as sameness of the
nodes[4].
Definition 3 helps us
systematically capture
necessary and sufficient
conditions of a particular
disease which roughly
corresponds to the so-
called “main pathological/
etiological conditions”.
Fig. 1 shows the main
types of diabetes consti-
tuted by corresponding
types of causal chains.
Assume, for example, that
(non-latent) diabetes and
type-I diabetes are respec- Fig. 1 Types of diabetes constituted of causal chains.
Fig. 2 A visual editing tool for causal
chains to define disease concepts. Fig. 3 A browsing tool for causal chains.
tively defined as and
. Then, we get according to Definition 3.
3 Development of a disease ontology and a browsing tool for it
Based on the model discussed in the previous section, we developed a disease
ontology. Its design policy and conceptual structures of the ontology were decided
through repeated discussions by ontology engineer and medical expert. We defined its
upper level concepts (classes) such as clinical disorders (abnormal states), causal
chains, causal relationships (cause and effect), etc. based on YAMATO 1 . Disease
concepts were defined sub-class of these concepts by clinicians in 12 special fields.
Although the disease ontology are developed using Hozo 2 , we developed a visual
editing tool for it so that clinicians can easily edit the definition of disease concepts.
Fig. 2 shows its user interface. It visualizes causal chains defined in a selected disease
as directed graph like Fig.1. In the graph, nodes represent clinical disorders and links
represents causal relationships between them. When users edit the graph, it automati-
cally translated into ontology in Hozo’s format. The ontology could be exported in
OWL formats thanks to the export functions of Hozo.
Note here that each clinician defined disease concepts in his/her special field
without knowing how other diseases were defined in other filed by others. After they
finished defining disease concepts, we collected all causal relationships from all dis-
ease concepts defined in the 12 special fields. Then, we combined causal chains
which included the same clinical disorder. As the result, we obtained causal chains
which include about 17,000 clinical disorders defined in 6,000 diseases. They repre-
sent possible causal chains in human body.
In order to browsing these causal chains, we developed a browsing tool (Fig. 3).
It visualizes causal chains defined in the disease ontology and the user can browse
1
http://www.ei.sanken.osaka-u.ac.jp/hozo/onto_library/upperOnto.htm
2
http://www.hozo.jp/
them through some functions such as searching, tracing, changing layout, zooming etc.
Although it is implemented as a client application using Hozo’s ontology API, we
plan to develop web services version of it. We also consider publishing the disease
ontology as Linked Open Data with SPARQL endpoint to get their causal chains.
Currently, we focus on definitions of disease in order to provide a basic
knowledge for medial information systems without considering particular applications.
When we use the disease ontology for a specific application, we will consider making
some extension the ontology and browsing system according to the purpose.
4. Concluding Remarks
We developed a disease ontology based on River Flow Model and a browsing tool
for causal chains defined in it. Because the ontology is based on ontological consider-
ation of causal chains, it could capture characteristics of diseases appropriately. The
definition of disease as causal could be also very friendly to clinicians since it is simi-
lar to their understanding of disease in practice. Moreover, it could include richer
information about causal relationships in disease than other disease ontologies or
medical terminologies such as SNOMED-CT. Currently we are refining the ontology
through reviewing definitions of disease concepts. We are also organizing definitions
of clinical disorders into an abnormality ontology based on YAMATO. After these
refinement processes, the ontology could become more systematized knowledge.
Other future works includes development of a web service for browsing causal chains
and publishing the disease ontology as Linked Open Data.
The demonstration is available at the URL: http://www.hozo.jp/demo/
Acknowledgement
A part of this research is supported by the Japan Society for the Promotion of Science
(JSPS) through its “FIRST Program" and the Ministry of Health, Labour and Welfare,
Japan.
References
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Genomics 10(1):S6.
3. Cowell, L. G. and Smith, B (2010) Infectious Disease Ontology. Infectious Disease Infor-
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Buffalo, USA.
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ICBO2012