=Paper= {{Paper |id=Vol-1327/6 |storemode=property |title=Towards the Integration of Abnormality in Diseases |pdfUrl=https://ceur-ws.org/Vol-1327/icbo2014_paper_14.pdf |volume=Vol-1327 |dblpUrl=https://dblp.org/rec/conf/icbo/YamagataKIOM14 }} ==Towards the Integration of Abnormality in Diseases== https://ceur-ws.org/Vol-1327/icbo2014_paper_14.pdf 
                                                    ICBO 2014 Proceedings


  Towards the Integration of Abnormality in Diseases

 Yuki Yamagata, Kouji Kozaki                      Takeshi Imai, Kazuhiko Ohe                               Riichiro Mizoguchi
 Department of Knowledge Science                Department of Medical Informatics                  Research Center for Service Science
     I.S.I.R, Osaka University                    Graduate School of Medicine                      Japan Advanced Institute of Science
 8-1 Mihogaoka, Ibaraki, Osaka, Ja-                  The University of Tokyo                                and Technology
                pan                             7-3-1, Hongo, Bunkyo-Ku, Tokyo,                     1-1 Asahidai, Nomi, Ishikawa, Ja-
 yamagata@ei.sanken.osaka-u.ac.jp                             Japan                                               pan



    Abstract—Knowledge of abnormalities is important for un-            from our abnormality ontology to other biomedical concepts,
derstanding diseases. A number of resources provide terms relat-        we can establish mutually complementary relationships be-
ed to abnormalities in biomedicine. In this paper, we investigate       tween biomedical concepts in different levels, which will con-
the differences in the hierarchical structure of these biomedical       tribute to ensuring interoperability across biomedical resources.
resources and discuss issues of reuse and integration with regard       Our goal is to provide not only a theory for a better understand-
to abnormal states based on ontological theory. Then, we show a         ing of abnormal states but also useful information for clinical
solution for integrating them by linking abnormal states in our         practice. We are planning to integrate abnormalities in the def-
abnormality ontology to other resources according to the mean-          inition of diseases in the Department of Cardiovascular Medi-
ing of the concepts at each level.
                                                                        cine and other medical departments at the University of Tokyo
   Keywords—ontology; abnormality; disease
                                                                        Hospital in our ontology with external biomedical resources at
                                                                        each level of meaning as a concrete example.
                        I.   INTRODUCTION                                   This paper is organized as follows. In Section 2, we intro-
    In order to understand diseases, one must first capture the         duce our ontology of abnormalities. In Section 3, we examine
abnormal states in diseases adequately. They are observed as            the characteristics of biomedical resources, discuss the problem
symptoms by patients or as signs by clinicians in clinical find-        with them based on ontological theory and propose a solution
ings. Clinical test data can also provide evidence for the exist-       for integration with respect to abnormal states. Finally, in Sec-
ence of abnormal states. In addition, in basic research, through        tion 4, we present concluding remarks and a give an outline of
analysis of disease models of animals, many researchers make            future work.
efforts to understand how causative agents are related in the
etiological process. Moreover, abundant knowledge about ab-                             II. ABNORMALITY ONTOLOGY
normalities is available in scientific articles. The above obser-           This section provides an overview of our abnormality on-
vation shows that abnormality is a key factor for capturing             tology.
diseases, assisting in the interoperability between basic re-
search and clinical medicine for integrating a wide variety of          A. Three-Layer Ontological Model of Abnormal States
knowledge across domains. We have been involved in the de-                  In order to develop an is-a hierarchical tree of abnormali-
velopment of a disease ontology [1]. As part of this research,          ties, it is important to conceptualize them from a consistent
we have focused on abnormal states in the definition of diseas-         viewpoint. To this end, we have been developing an abnormali-
es and have rigorously systematized an abnormality ontology             ty ontology [2, 3] having a three-layer structure:
[2, 3].
                                                                           • Level 1: Generic abnormal states
    A number of terminologies and standard vocabularies have
been developed for many years, and recently, ontologies have               • Level 2: Object-dependent abnormal states
also been constructed in the biomedical domain. They offer                 • Level 3: Specific disease-dependent abnormal states
useful data, and some of these terms include abnormality con-
cepts. In order to make efficient use of these resources, we                The top-level categories define very basic and generic con-
need a solution that ensures interoperability between abnormal-         cepts, for example, "small in area," "hypofunction," etc., which
ity knowledge across domains. To achieve this, first, one has to        are commonly used not only in clinical medicine but also in
elucidate one's own perspectives of the resources before using          other domains. Level 2 concepts are dependent on objects. In
them and to make the meanings of concepts explicit.                     the lower level of the tree, concepts are designed to represent
                                                                        abnormalities at specific human organ / tissue / cell levels. For
In this paper, we discuss differences of existing biomedical            example, by specializing "small in area" at Level 1, " narrowed
resources based on ontological engineering theory with respect          cross-sectional area of tube ", where the cross-sectional area of
to abnormalities. Next, we investigate the relationships be-            a tubular structure has become narrowed, is defined at Level 2,
tween abnormal states in our abnormality ontology and corre-            and this is further specialized in the definitions "vascular steno-
sponding terms in biomedical resources. By mapping concepts             sis" (blood vessel-dependent), "arterial stenosis", "coronary




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artery stenosis" (coronary artery-dependent), and so on. Level 3                         form. For example, in terms of the state of hypertension 3, our
concepts are captured as specific disease-dependent (context-                            model ensures interoperability among the forms , , and
stenosis" at level 2 is defined as a constituent of ischemic heart                       . Therefore, our model realizes interopera-
disease at Level 3. In our ontological approach, common con-                             bility between test data and abnormal states in the definition of
cepts can be kept distinct from specific ones and can be appro-                          diseases. In practice, we found that more complicated terms,
priately defined according to their context.                                             such as adding modifier words (e.g., transient hypertension),
                                                                                         and three types of compound words (e.g., “blood pressure” as
    As of 11 May 2013, our ontology has 21,669 abnormal                                  an OA type: < blood (O), pressure (A)>). We have developed a
states constituted of 6,302 diseases across 13 medical depart-                           guideline and deal with them as a variation of data representa-
ments, and among them, clinicians have currently refined con-                            tion.
cepts of 9,985 abnormal states constituted of 1,602 diseases
across five major departments.                                                                    III. UTILIZATION OF BIOMEDICAL RESOURCES
B. Representation of Abnormal State                                                          In this section, we investigate the hierarchy of "coronary ar-
    In medicine, abnormal states are interpreted from the di-                            tery stenosis" in existing biomedical resources and perform a
verse perspectives of specialists such as clinicians, pathologists,                      comparison between our ontology and existing biomedical
biologists, geneticists, and so on, and correspondingly a variety                        resources to make them interoperable with each other with
of representations of abnormal states are used. Therefore, we                            respect to abnormalities.
classified the abnormal states into three categories: a property 1                       A. Charestictics of biomedical resources
(e.g., hypertension), a qualitative representation (e.g., blood
                                                                                            1) SNOMED-CT and MeSH Terminologies
pressure is high), and a quantitative representation (e.g., blood
pressure 180 mmHg). In previous work, we proposed a Proper-                                   a) SNOMED-CT: Systematized Nomenclature of Med-
ty-Attribute interoperable representation framework for ab-                              icine-Clinical Terms (SNOMED-CT) is a clinical terminology
normal states [2, 3] on the basis YAMATO [4].                                            first developed by the College of American Pathologists
                                                                                         (CAPs) and is currently maintained by the International Health
     We captured all abnormal states as properties2 represented                          Terminology Standards Development Organization (IHTSDO)
by a tuple: , e.g., . We specified the property by decomposing it into a tu-
                                                                                         lection of clinical terms that contain more than 310,000 con-
ple: . The Attribute Value
can be either a Qualitative Value (Vql) or a Quantitative Value                          cepts. It is widely used as an international standard vocabulary.
(Vqt). For example, "arterial stenosis" is decomposed into                               SNOMED-CT has a hierarchical structure. The root concept of
 as a qualitative repre-                          the hierarchy is named "SNOMED-CT," and there are 19 top-
sentation, or  as a quan-                          level categories, including Clinical finding/disorder, Body
titative representation. Then, we introduce "Object" to identify                         structure, Organism, Substance, and other things important for
the target object, and we represent an abnormal state as a triple:                       clinical health. Most of the abnormal states are included in the
. This is the                            category Clinical finding/disorder. In the hierarchical tree,
basic form in our representation model of abnormalities. In                              concepts are linked by is-a relationships. SNOMED-CT al-
addition, we introduce "Sub-Object (SO)" as an advanced rep-                             lows multiple inheritances. In addition, one concept can have
resentation for what will be focused on. For example, in the                             relationships other than is-a, like "finding site," "method,"
case of "hyperglycemia", since the glucose concentration (A)                             "clinical course" and so on, to connect concepts between dif-
means the ratio of the focused object (SO) relative to the whole                         ferent categories, e.g., "occlusion of artery has finding site
mixture (O), the representation of "hyperglycemia" is a quad-                            arterial structure."
ruple, .
In another case of an advanced representation, "Colonic poly-                                 b) MeSH: Medical Subject Headings (MeSH) is a the-
posis" is described as . Our model can deal with both clinical test data and                           Medicine and is used for indexing biomedical articles [6]. The
abnormal states in the definition of diseases. The clinical test                         2014 MeSH contains 27,149 terms as descriptors (MeSH
data can be represented in the form  (OAVqt), which can be converted                                chical tree called "MeSH tree" is organized into 16 categories,
into a property representation form  (OPVp) via a qualitative representation                             Most of the concepts of abnormal states are classified into the
                                                                                         diseases category. MeSH also allows multiple inheritances.
                                                                                            2) PATO and HPO Phenotype Ontologies: In biomedical
                                                                                         domains, scientists observe entities through experiments or
1
  The property discussed here is based on ontological theory, not Web Ontol-             clinical findings to capture abnormal states. Therefore, repre-
ogy Language (OWL) property which means a link (relation) between two
nodes.
2                                                                                        3
  A state derived from or associated with a property (P) is defined as: "a                 Our model can describe the mechanism “hypertension” more specificity by
temporal entity derived by a time-indexed property (P) in which the bearer               using other factors; e.g., accumulation of atheroma in arterial wall →decreased elasticity of
pant.                                                                                    arterial wall →hypertension.




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sentations of knowledge about abnormalities are usually given           results in multiple inheritance from two parents in the PATO
as phenotypes.                                                          hierarchical tree: one is abnormal states, and the other is prop-
    Phenotypic Quality Ontology (PATO) is an ontology of                erty (quality). For example, "decreased area" in PATO has two
phenotypic qualities for annotating biological phenotypes               super-classes, "decreased size" and "area" (Fig. 1). Such mul-
across species [7]. PATO provides qualities to describe pheno-          tiple inheritance makes things complicated, and it is difficult
typic information, such as size, color, weight and so on, and           to understand the underlying meaning of concepts. If comput-
qualities have abnormal states as subclasses of each quality; for       ers integrate PATO concepts and other resources in a naïve
example, size has lower concepts like "increased/decreased              way, inappropriate consequences might be derived unexpect-
length," "increased/decreased volume," "dwarf-like", in addi-           edly. For example, HPO provides the concept of "coronary
tion to length, area, and volume. PATO is widely used as a              artery stenosis," and an incorrect relation such as "coronary
standard vocabulary for biological measurement in bio-                  stenosis is-a area" might be derived by a naïve combination of
communities. PATO also plays a role as a reference ontology             PATO and HPO. "Area" is not an abnormal state. In our on-
for databases of specific species, such as Drosophila [8].              tology, parameters such as pressure, area, concentration, and
    The Human Phenotype Ontology (HPO) is an ontology for               so on, are properly dealt with as an attribute (A), so that there
human specific phenotypes [9]. It has been developed using              is no possibility of deriving inappropriate consequences such
phenotypic information from the human genetic diseases data-            as the one shown above.
base Online Mendelian Inheritance in Man
(OMIM) [10] and medical articles, and provides
over 10,000 terms. HPO classifies human pheno-
types into three categories: 1) mode of inher-
itance, 2) onset and clinical course, and 3) phe-
notypic abnormalities. Abnormal state concepts
are mainly subclasses of 3) phenotypic abnor-
malities.
   3) LOINC: Logical Observation Identifiers,
Name and Codes (LOINC) was developed by the
Regenstrief Institute as a universal code for ex-
changing clinical test data [11]. It is widely used
in 185 countries. LOINC provides six fields
(component (analyte), property, timing of the
measurement, sample type, scale type, and
method). By combining the contents of each
field with colons, the name of a clinical test can
be described. For example, "a test for glucose                                   Fig. 1 Is-a hierarchy of coronary artery stenosis.
tolerance about after 2 hours serum glucose for                             With respect to the intrinsic nature of abnormal states,
100g oral" is represented by "GLUCOSE^2H POST 100 G                     states can change, and their essential characteristic is under-
GLUCOSE PO:MCNC:PT:SER/PLAS:QN."                                        stood with respect to how the corresponding attribute (A) has
B. Ontological Issues and Solution with Regard to                       the value (V), which can change as time goes. Then, one can
    Abnormality                                                         find that the authentic "is-a" relation should be "decreased
   1) PATO and HPO: Both PATO and HPO representations                   area is-a decreased size" rather than "decreased area is-a area",
are based on property representations in accordance with the            and our model adopts the former.
upper ontology Basic Formal Ontology (BFO) [12]. Therefore,                 Next, we discuss ontological issues in HPO. The HPO hi-
one could say that these representations are similar to our             erarchy is based primarily on where the abnormal states occur
property representation of abnormal states. However, in PATO            (e.g., arterial stenosis is-a abnormalities of peripheral arteries).
and HPO, the relationship between Property (P) and Attribute            Therefore, it is difficult to know the is-a relation of "stenosis"
(A) & Value (V) is unspecified, which leads to a lack of in-            comprehensively. In the HPO tree, ad hoc creation of correct
teroperability with clinical test data. The data values are most-       is-a relationships is found, such as "renal artery stenosis is-a
ly quantitative, and clinical test data are represented in terms        arterial stenosis." To make matters worse, HPO has no generic
of A and V. To achieve interoperability with clinical test data,        concepts at the upper level nor any information about what
not only the OP form but also the OAV form is required. Our             attribute takes what value, and we cannot capture the essential
model can deal with both representation forms and can                   property of the state as "stenosis." Furthermore, specialization
achieve interoperability between OP and OAV, which will be              using the part-whole relation appears in the is-a hierarchy, e.g.,
of great assistance in medical practice                                 , which is misleading.
(P) from Attribute (A) in the same way as BFO does, which




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    With respect to abnormal states, it is important to capture                   2) SNOMED-CT and MeSH: The development of
the identity of the state by revealing "where and how its intrin-             SNOMED-CT and MeSH started before ontological engineer-
sic attribute takes a particular abnormal value." On the basis of             ing had become mature, and thus they have some problems
such understanding, our ontology adopts a single is-a relation-               ontologically [14]. SNOMED-CT and MeSH do not have any
ship to inherit the intrinsic nature of abnormal states. For ex-              formal upper ontology. This allows for multiple inheritance
ample, all lower classes of "small in area" inherit the property              that results in complicated relations. Such a structure may lead
of "the area (A) is small (V)," such as" narrowed cross-                      to inconsistencies in daily clinical practice, since the organiza-
sectional area of tube," "vascular stenosis," and so on.                      tion of concepts is not principled, and hence relationships
    In practice, however, only a single hierarchical structure                among concepts lack consistency. As shown in Fig. 1, in
seems incompatible with various perspectives in biomedicine.                  SNOMED and MeSH, abnormal states and diseases are mixed
In order to support different perspectives, we developed a                    up in the hierarchy, as is the case in HPO. In MeSH, the upper
technology for dynamically generating an on-demand classifi-                  class "coronary stenosis" is a disease, namely, "coronary dis-
cation hierarchy [13]. For instance, if researchers want to                   ease," and at an even higher level, diseases are defined, like
know the classification of abnormal states in terms of anatom-                "heart disease" and "cardiovascular disease." In SNOMED,
ical structure, by using the is-a relation of Object (O) for each             the upper concept of "coronary artery stenosis" is a "coronary
abnormal state, a subclassification according to anatomical                   occlusion," and furthermore, at even higher levels, there are
structure abnormalities can be generated (Fig. 2). The parton-                various concepts, such as "heart disease," "vascular (blood
omy of organs can also be used for generating a hierarchy,                    vessel) finding," "disorder of cardiovascular system," "disor-
which is similar to the part-of-whole relationship of anatomi-                der of soft tissue" and so on.
cal structure: e.g., "atrium abnormality" is classified as a sub-                 SNOMED-CT terms have been used for electronic health
category of "cardiac abnormality."                                            record (EHR) systems. Because SNOMED-CT does not dif-
                                                                              ferentiate abnormal states from diseases, serious clinical prob-
                                                                              lems may occur. For example, imagine a case where a clini-
                                                                              cian examines an angiographic image and, by using the
                                                                              SNOMED-CT term "coronary artery stenosis," records it for
                                                                              evidence of "abnormal states" in the EHR system. Due to in-
                                                                              heriting properties from one of the upper concepts in
                                                                              SNOMED-CT, namely, "disease," there is a possibility of
                                                                              deriving an erroneous consequence that the "abnormal state" is
                                                                              a "disease." Our ontology makes a distinction between diseas-
                                                                              es, disease-dependent abnormal states, and disease-
                                                                              independent abnormal states. Therefore, there is no possibility
     Fig. 2 Reorganization of is-a relationships of abnormal states in
                                                                              of deriving inappropriate consequences, which is important for
     terms of anatomical structure.
                                                                              computer processing. In order to manage high-level clinical
    Another critical issue with HPO is that HPO does not dif-                 knowledge in EHR, we need a reliable method for representa-
ferentiate abnormal states from diseases and organize them in                 tion, and our ontological model provides us with high reliabil-
one hierarchy, which may be misleading and confusing in                       ity and sophisticated technology for realizing interoperability
clinical practice. In HPO, the parent of "coronary stenosis" is               between heterogeneous pieces of clinical information.
considered to be not an abnormal state but a disease, namely,                     3) LOINC: LOINC was excluded from the above-
"coronary disease." However, as shown in Fig. 1, "coronary                    mentioned comparative research, because LOINC does not
disease" has three parents: "arterial stenosis," "coronary artery             deal with concepts related to abnormal states.
abnormality", and "atherosclerosis," which seem to be abnor-                      LOINC provides the form O (So) A and is useful for in-
mal states. Therefore, there is no guarantee that a computer                  teroperability among various clinical test data. However,
can derive the correct answer to the question of whether "cor-                LOINC does not have Value (V). To realize interoperability
onary artery stenosis" is a disease or an abnormal state.                     between clinical test data and abnormal states, a Quantitative
    Our ontology enables us to make a distinction between ge-                 Value (Vqt) is needed in the representation form. Our model
neric abnormal states, object-dependent ones, and disease-                    can deal with quantitative data in the OAV form, and, there-
specific ones with a unified representation and allows us to                  fore, we can transform it into the OP form of abnormal states.
specialize concepts to the required granularity with consisten-               As a result, our model has the ability to maintain interoperabil-
cy. We conceptualize a disease as an entity represented in                    ity between clinical test data and abnormal states in diseases.
terms of abnormal states, and we deal with abnormal states                        Some readers might think, "Why not reuse other resources
and diseases as different entities. Therefore, we can focus on                such as clinical terminology or existing ontologies?", because
the intrinsic nature of the states themselves from one view-                  it seems that by combining existing resources, many abnormal
point and can develop an ontology from the viewpoint of state,                states would be easily covered. Such good candidates for reuse
without mixing up the viewpoint of disease.                                   concerning abnormal states are PATO, HPO, LOINC, and




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SNOMED-CT. As we illustrated in the problem above, just                       Mapping our ontology to other resources for integrating
reutilizing existing resources cannot integrate all knowledge             various data related to abnormalities will bring benefits to the
about abnormal states. It is too difficult to find all inappropri-        users of other resources, too. First, one can find concepts from
ate usages of is-a and resulting misclassifications from the              generic to specialized terms easily by referring to the single is-
                                                                          a tree in our abnormality ontology. For example, although
huge and heterogeneous system of concepts and to make an
                                                                          HPO does not care about consistent is-a relationships in terms
effort to modify them. Furthermore, since each resource has its           of "stenosis", by referring to "arterial stenosis" at Level 2 in our
own viewpoint, integrating them into a unified perspective                ontology through mapping, HPO users can get the is-a relation-
must be a hard task because it necessarily requires compara-              ships: "arterial stenosis is-a vascular stenosis is-a narrowed
tive analysis and validation of accuracy in integrated concepts.          cross-sectional area of tube is-a small in area." Since "small
    Unified theoretical considerations have resulted in interop-          area" is linked to a PATO concept, via our ontology, users
erability between various representation forms, which will                might find orthologous concepts of other species. Specifically,
enable us to establish a computer-understandable model for                human phenotypes can be linked to the phenotypes of model
abnormal states. We need more sophisticated organization of               organisms, e.g., mouse, rat, etc., if the set composed of Attrib-
related representations, including quantitative and qualitative           ute (A) and Value (V) are identical, and the Object (O) has
                                                                          structural similarity. PATO2YAMATO aims to integrate phe-
data and knowledge at higher levels of abstraction about ab-
                                                                          notype descriptions residing in differently structured compari-
normal states in the definition of diseases to exploit all of them        son contexts [16]. By applying PATO2YAMATO, mapping of
in a consistent manner. Our model is the first one to make                concepts across species and integrating knowledge from vari-
such exploitation possible, and will be of great assistance in            ous species may be possible.
medical practicee.
    The differences of resources are summarized in Table 1.

       TABLE I.       COMPARISON OF BIOMEDICAL RESOURCES




C. Integration of biomedical abnormal states
                                                                                   Fig. 3. Integration of abnormal states in biomedicine.
   1) Integration of abnormal states
                                                                             2) Integration of components of abnormal states
    As illustrated in Section 2, our ontology provides three lev-
els of abnormal states from generic to disease-specific.                      We are also planning to link the components of the  representation of abnormal
    Level 1 in our ontology defines generic concepts corre-               states to other resources. We will try to connect the Object (O)
sponding to the PATO concepts, and our Level 1 concepts can               to concepts in FMA (The Foundational Model of Anatomy
be mapped to related PATO concepts (Fig. 3). The lower Level              ontology) [17].
2 concepts are human anatomical structure-dependent abnor-
mal states, which correspond to the HPO concepts. By creating                3) Integration of biomedical articles
links between Level 2 concepts and HPO concepts, it will be                   By mapping MeSH terms, it will be possible to retrieve bi-
possible to navigate from the HPO concepts to the upper gener-            omedical articles related to abnormal states or diseases. If we
ic concepts of PATO. Level 3 provides disease-specific ab-                obtain new findings of the constituents of diseases, we can add
normal states, such as "myocardial ischemia in ischemic heart             new relationships to the causal chains of the diseases, which
disease," "chest pain in angina pectoris", and so on. In the re-          might contribute to the elucidation of the etiological mecha-
vised version 11 of the International Classification of Diseases          nism. Our mapping is also useful for MeSH users to understand
(ICD), diseases contain information of "causal properties" [15],          how their research subjects are involved in various abnormal
and therefore, we are planning to map our Level 3 concepts to             states in the human body in diseases. This will contribute to the
the corresponding concepts in the ICD. Level 3 abnormal states            progress of biomedical research.
are described in the causal chains of diseases [1]. By mapping
our disease concepts of disease ontology to the ICD, ICD users                              D. Concluding Remarks
can understand the causal relationships of the abnormal states                A large volume of data and concepts related to abnormal
in diseases. Our ontology can also allow users to navigate re-            states is currently available in existing resources. However,
lated concepts in other resources, such as HPO, PATO, etc.                there are no resources that cover all levels of abnormal states




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from generic to disease-specific. We performed a comparison               Linked Data (LD)[18], and have made available a browsing
between our ontology and existing resources, and identified the           system that links our data to DBPedia [19] and 3D images of
issue that the heterogeneous levels of meanings in the different          related anatomical parts provided by BodyParts3D [20]. Next,
resources and multiple perspectives prevented us from reusing             we are planning to map our abnormal states to other resources
and integrating them. This motivated us to develop an abnor-              using LD and to scale up to applications requiring more com-
mality ontology from the generic level to the disease-specific            plicated knowledge.
level.
                                                                                                    ACKNOWLEDGMENT
    Our medical ontology project started seven years ago. Since
then, it has been refined and revised several times by discus-                A part of this research is supported by the Japan Society for
sion with both ontologists and clinicians. Our ontology will              the Promotion of Science (JSPS) through its “FIRST Pro-
play a role in proper guidelines for giving an ontological point          gram”" and the Ministry of Health, Labour and Welfare, Japan.
of view in various controlled vocabularies, and will lead to the          The authors are deeply grateful to Drs. Ryota Sakurai, Natsuko
development of a consistent hierarchical structure with a uni-            Ohtomo, Aki Hayashi, Takayoshi Matsumura, Satomi Terada,
fied representation. Mapping our ontology with other resources            Kayo Waki, and other, at The University of Tokyo Hospital for
at each level of meanings will contribute to ensuring interoper-          describing disease ontology and assisting us with their broad
ability across biomedical resources. Since our approach allows            clinical knowledge. We also would like to thank other team
users to navigate from generic concepts to specific concepts in           members, Drs. Yoshimasa Kawazoe, Masayuki Kajino, and
other domains by following links, it offers complementary                 Emiko Shinohara from The University of Tokyo for useful
information. We are currently applying the concepts of abnor-             discussions related to biomedicine.
mal states in the definition of diseases in the Department of                                           REFERENCES
Cardiovascular Medicine and several other departments at The
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tion of diseases in other medical departments in our ontology.                 Ontology project: linking molecular biology and disease through
For example, "increased blood CK concentration in muscular                     phenotype data,” NAR, vol. 42 (Database issue), pp.D966-74. 2014.
dystrophy" used in the Neurology Department has commonali-                [10] OMIM [http://www.ncbi.nlm.nih.gov/omim]
ty with "increased blood CK concentration," and moreover,                 [11] LOINC [http://loinc.org/]
"increased blood cholesterol concentration in hyperlipidemia"             [12] Grenon P, Smith B, Goldberg L, “Biodynamic ontology: applying BFO
has commonality with the same generic concept, "increased                      in the biomedical domain,” Stud Health Technol Inform, vol. 102, pp.
concentration." We are planning to map the disease "acute                      20-38, 2004.
myocardial infarction" in our disease ontology to "acute myo-             [13] K Kozaki et al., “Dynamic Is-a Hierarchy Generation System Based on
cardial infarction" in the ICD. In our ontology, diseases are                  User's Viewpoint,” in procceing of JIST, LNCS 7185, pp.96-111, 2012.
defined as causal relationships of abnormal states, and from              [14] S Schulz, B Suntisrivaraporn, F Baader, “SNOMED CT's problem list:
this mapping, ICD users will be able to know the causal rela-                  Ontologists' and Logicians' therapy suggestions,” Stud Health Technol
                                                                               Inform. vol.129(Pt 1), pp:802-806, 2007;.
tionship, "decreased blood flow" causes "myocardial ische-
                                                                          [15] ICD 11 [I http://www.who.int/classifications/icd/revision/en/]
mia," which results in "myocardial necrosis" in acute myocar-
                                                                          [16] H Masuya, R Mizoguchi: “An advanced strategy for integration of
dial infarction. This causal relationship is also available to                 biological measurement data ,” in ICBO2011, 2011, pp. 79-86.
users of HPO by following the linked concept of abnormal
                                                                          [17] C Rosse, JVL Mejino. “A reference ontology for biomedical
states. With respect to "myocardial ischemia," by mapping this                 informatics: the Foundational Model of Anatomy,” J Biomed Inform,
to the MeSH term "myocardial ischemia," we can collect arti-                   vol. 36, pp: 478-500., 2003.
cles that are related to the concept.                                     [18] K Kozaki, Y Yamagata, T Imai, K, et al., “Publishing a disease
                                                                               ontologies as linked data,”. in Proc. of JIST2013, 2014, pp. 110-128．
    In this way, our approach allows users to navigate various
                                                                          [19] DB pedia [http://dbpedia.org/]
abnormality knowledge across domains, which will create a
                                                                          [20] BodyParts3D,       The     Database       Center   for   Life    Science
bridge between basic research and clinical medicine. We pub-                   [http://lifesciencedb.jp/bp3d/]
lished the causal chains in our disease ontology in the form of




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