=Paper=
{{Paper
|id=Vol-2753/paper39
|storemode=property
|title=Mathematical Modeling of Diagnosis and Diagnostic Information Space of Chinese Image Medicine for their Unified Representation in Information Systems for Integrative Scientific Medicine
|pdfUrl=https://ceur-ws.org/Vol-2753/short13.pdf
|volume=Vol-2753
|authors=Serhii Lupenko,Oleksandra Orobchuk,Igor Kateryniuk
|dblpUrl=https://dblp.org/rec/conf/iddm/LupenkoOK20
}}
==Mathematical Modeling of Diagnosis and Diagnostic Information Space of Chinese Image Medicine for their Unified Representation in Information Systems for Integrative Scientific Medicine==
https://ceur-ws.org/Vol-2753/short13.pdf
Mathematical Modeling of Diagnosis and Diagnostic
Information Space of Chinese Image Medicine for their Unified
Representation in Information Systems for Integrative Scientific
Medicine
Serhii Lupenkoa, Oleksandra Orobchukb and Igor Kateryniukc
a
Ternopil Ivan Puluj National Technical University, Ruska str., 56, Ternopil, 46001, Ukraine
b
Ternopil Ivan Puluj National Technical University, Ruska str., 56, Ternopil, 46001, Ukraine
c
Inlimited, 3G Mihayla Maksimovicha str., Kyiv, 03022, Ukraine
Abstract
The article is devoted to mathematical modeling of the diagnostic space of Chinese image
medicine, which is an important stage in the development of an integrated onto-oriented
information-analytical environment of research, professional healing and e-learning of
Chinese image medicine, which is a representative of unconventional medical areas and a
promising component of integrative scientific medicine. A unified model of diagnosis in
Chinese image medicine has been developed, which is a function of topological and
nosological ontologies, as well as ontologies of methods and ontologies of metrics and scales
in Chinese image medicine.
Keywords 1
Mathematical modeling, diagnostic space, ontology, information system, Chinese image
medicine, integrative scientific medicine.
1. Introduction
The modern world is characterized by rapid progress in the information-communication
technologies development , which significantly affects all industries, and in particular medicine, and
also becomes a determining factor in the successful formation of a promising medical field -
integrative scientific medicine. Integrative medicine is developing worldwide, meeting the demand of
the international community for the development of alternative and complementary medicine through
the integration of the achievements of folk medical practices on an inter-complementary basis and
based on the principles of evidence-based medicine [1-3]. Its prospects are due to the application of
individual norms, holistic (integral) approach to the patient at the theoretical and applied levels,
emphasis on prevention and rehabilitation of the body by activating its internal potential, complex
substrate-energy-information nature of diagnostic and therapeutic procedures, their efficiency and
economy.
In China, integrated medicine has become an integral part of the public health system, successfully
combining the achievements of Western medicine and traditional Chinese medicine. In recent years,
there has been a growing interest in the research of methods and tools of Chinese imaging medicine
(CIM), which is a component of traditional Chinese medicine [4], has deep historical roots, is actively
spreading and developing around the world (China, USA, Canada, Brazil, Switzerland, Germany,
Hungary, Slovakia, Ukraine, Czech Republic, Latvia, Estonia, Russia, etc.). Beijing Kundawell
Medical Research Institute (China) is a world-famous center for teaching and researching Chinese
imaging medicine.
IDDM’2020: 3rd International Conference on Informatics & Data-Driven Medicine, November 19–21, 2020, Växjö, Sweden
EMAIL: lupenko.san@gmail.com (S. Lupenko); orobchuko@gmail.com (O. Orobchuk); igor.kateryniuk@gmail.com (I. Kateryniuk)
ORCID: 0000-0002-6559-0721 (S. Lupenko); 0000-0002-8340-913X (O. Orobchuk); 0000-0002-9542-6279 (I. Kateryniuk )
©️ 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
Due to the ancient Chinese origin of CIM, most of its diagnostic and therapeutic methods are
purely intuitive, empirical, and difficult to formalize; knowledge is fuzzy, poorly structured and
polysemantic [5]. All this is an obstacle to the creation of a full-fledged scientific paradigm of CIM in
medicine, as many theoretical and experimental aspects and patterns of this medical field of folk
medicine remain unclear. To reveal the root causes and mechanisms of human diseases, unification
and formalization of traditional CIM theory knowledge and the creating of its qualitative scientific
theory, the Research Program of Chinese Image Medicine for 2017-2023 was developed [6]. The
program is aimed at solving a problems range of theoretical, clinical, experimental and information-
analytical areas. One of the current scientific and applied tasks of the Program, as well as the strategy
of the World Health Organization in the field of folk medicine [7], is to create an integrated onto-
oriented information-analytical environment of research, professional healing and e-learning CIM, the
core of which will be an ontology of diagnostic and therapeutic methods of CIM. An important stage
in the development of an integrated information environment for CIM is the creation of mathematical
and software tools for a unified presentation and specification of diagnostic patient information,
which is obtained by both Western scientific medicine and CIM. This work, in fact, is devoted to the
creation of a mathematical model of diagnosis and diagnostic space CIM for their representation in an
integrated onto-oriented information-analytical environment.
2. Main part
The developing purpose an information-analytical environment for CIM, the generalized
architecture of which is presented in Fig.1 [8], is to ensure effective organization and coordination of
existing CIM-therapists, CIM researchers, people studying CIM, as well as the formation of modern
intellectualized information means and resources in the field of folk, complementary and integrative
medicine at both national and international levels [9].
Figure 1: General architecture of integrated onto-oriented information analytical environment of
scientific researches, professional healing activities and e-learning of Chinese image medicine
Diagnostic information about the patient in an integrated onto-oriented information-analytical
environment of scientific research, professional healing and e-learning of Chinese imaging medicine
is proposed to provide according to table 1.
Table 1
Diagnostic information types in the integrated information analytical environment for CIM
Personal information Self-assessment information (physical and
(age, gender, family members, etc.) psychological condition) of the patient before
and after therapy, using methods of psychological
scaling
Medical information about the patient includes Diagnostic information obtained by TCM and CIM
information obtained by methods of methods, namely, the results of diagnosis by TCM
conventional (Western) medicine, namely, (examination, listening, palpation diagnosis
medical history and results of medical results), the results of energy diagnosis by hand
examinations (laboratory tests, results of and (or) body, the results of internal imaging
functional diagnostics, doctor's report, etc.) ("eye of the mind", "second heart")
If the formation of personal information about the patient, his medical data obtained by
conventional Western medicine (medical history, laboratory tests, results of functional diagnostics,
etc.), the patient's self-rating information does not contain fundamental difficulties, the formation of
diagnostic information by CIM methods is difficult scientific problem, the solution of which is
possible by developing new mathematical models, methods and means of presenting diagnostic
information obtained by CIM methods.
Creation of diagnostic space 𝑿 CIM should be preceded by the development of a diagnostic
ontology 𝑶𝐷 CIM (ontology of the theory and technologies of diagnosing in CIM), which as its sub-
ontology should include nosological ontology 𝑶𝑁 CIM, topological diagnostic ontology 𝑶𝑇 CIM,
and ontology 𝑶𝑀 methods of obtaining and specifying sensory-image diagnostic information
(diagnostic methods ontology ) in CIM, as well as an ontology 𝑶𝑆 diagnostic metrics and scales in
CIM. 130 concepts of CIM were selected and used to build these ontologies.
From a formal point of view, the diagnostic ontology of CIM can be presented as such four of its
sub-ontologies:
𝑶𝐷 = {𝑶𝑇 , 𝑶𝑁 , 𝑶𝑀 , 𝑶𝑆 }, (1)
Topological diagnostic ontology 𝑶𝑇 CIM displays information about the topological localization
of diseases. Nosological ontology 𝑶𝑁 CIM reflects knowledge of the diseases types (classes) that are
accepted in the diagnostic theory of CIM.
Ontology 𝑶𝑀 diagnosing methods in CIM reflects knowledge of obtaining methods and
specifying sensory-diagnostic information in CIM. Diagnostic methods are the basis for perception
and identification (characterization) of the patient's condition in terms of nosological taxonomy of
CIM.
Creating such ontologies (taxonomies) is a difficult task and requires some thorough research.
Some issues of ontological modeling are considered in [10].
Information on the components of the diagnostic ontology of CIM is grouped in table 2.
Table 2
Components of the CIM diagnostic ontology
Components of the CIM Components description of the diagnostic CIM ontology
diagnostic ontology
NOSOLOGICAL ONTOLOGY Nosological ontology of CIM reflects knowledge about types
CIM (classes) of diseases which are accepted in the CIM diagnostic
theory
TOPOLOGICAL DIAGNOSTIC Topological diagnostic ontology CIM reflects information on the
ONTOLOGY CIM topological localization of diseases involving the physical body,
energy system (field system, Qi system) and information systems
(psycho-mental-spiritual system, Shen system) of human, in
particular, contains information about body parts, organs ,
physical body tissues , information about bioactive points and
energy channels of the human energy system, information about
informational, psycho-emotional, mental and spiritual
topological aspects of man.
ONTOLOGY DIAGNOSIS The ontology of diagnostic methods in CIM reflects knowledge
METHODS OF CIM about methods (channels) of receiving and specifications of
sensory-image diagnostic information in CIM.
ONTOLOGY OF DIAGNOSTIC Describes the quantitative characteristics (indicators) of the
METRICS AND SCALES IN CIM diagnostic space of CIM, which determine the disease
manifestation degree and can be set to a certain numerical (for
example, from 1 to 5) or non-numerical (for example, very weak,
weak, medium, strong, very strong) scale.
At the most abstract level, the diagnostic space can be represented as a set of all possible diagnoses
in the CIM, and each individual diagnosis will be presented as a result of design according to a certain
procedure 𝑭(∙) from the ontologies described above. In this case, the CIM diagnostic space is a
function of the ontologies 𝑶𝑇 , 𝑶𝑁 , 𝑶𝑀 , 𝑶𝑆 , namely:
𝑿 = 𝑭{𝑶𝑇 , 𝑶𝑁 , 𝑶𝑀 , 𝑶𝑆 }, (2)
where 𝑭(∙) – a certain type of method (algorithm, procedure) formation of the diagnosis in CIM from
the corresponding ontologies 𝑶𝑇 , 𝑶𝑁 , 𝑶𝑀 , 𝑶𝑆 .
The creation of the above ontologies and taxonomies is the first step in building a model of the
diagnostic space CIM. The next research stage is to build a procedure for forming a diagnostic space
for diseases ontology in CIM, topological ontology, ontology of diagnostic methods and ontology of
metrics and scales in CIM.
The taxonomy can be graphically represented as an ordered root tree, the nodes of which are
glossary-concept classes, and the edges will reflect the inclusion ratio. This method of graphical
taxonomies representation is often used in ontology development environments. For convenience
CIM diagnostic space formation from the above taxonomic trees it is necessary to carry out coding of
their tops. We will use this principle of coding nodes of taxonomic trees. Each taxonomic tree is
assigned a serial number 𝑛0 from 1 to 4. For example, to ontology 𝑶𝑇 is assigned number 1 (𝑛0 = 1),
to ontology 𝑶𝑁 is assigned number 2 (𝑛0 = 2), to ontology 𝑶𝑀 is assigned number 3, and to
ontology 𝑶𝑆 is assigned number 4. The upper node (root) of the taxonomic tree will be denoted by a
number equal to the ordinal number of the corresponding taxonomic tree (corresponding ontology).
Each level of a taxonomic tree is encoded by an integer equal to the number of its appearance in the
direction from the top (root) of the tree to its branches. In general , 𝑘-th level of a taxonomic tree with
a serial number 𝑛0 is presented as a combination of numbers separated by a period n0 . 𝑘1 . , … 𝑘𝑖 ,
where the first component displays the ordinal number of the taxonomic tree, and 𝒊 displays the
ordinal number of the level in this taxonomic tree. For any node, the last digit indicates its sequence
number at this level from its ancestor, and all previous digits indicate the ancestor node. Figure 2
shows an example of coding taxonomic tree nodes according to the above approach.
Figure 2: Coding example of taxonomic tree nodes for CIM
Based on the above, we create a procedure for forming a vector 𝑋 = (𝑥1 , 𝑥2 , … , 𝑥𝑁 ) information
diagnostic space 𝑿 Chinese image medicine, which is a unified formalized representation of the
diagnosis by CIM methods. Each 𝒊 −th component 𝑥𝑖 of vector 𝑋 = (𝑥1 , 𝑥2 , … , 𝑥𝑁 ) reflects the
detected by the CIM-therapist deviation from the norm in the physical system, energy system and
information system of the diagnosed patient. The total number of 𝑁 components of the diagnostic
vector 𝑋 = (𝑥1 , 𝑥2 , … , 𝑥𝑁 ) for each patient, in general, will be different, because it is the number of
detected by the CIM-specialist deviations (diseases, pathogenic factors) in a particular patient, which
are presented in terms of constructed nosological and topological taxonomies of CIM.
In general, 𝒊 −th component 𝑥𝑖 of vector 𝑋 = (𝑥1 , 𝑥2 , … , 𝑥𝑁 ) is four formal objects:
𝑥𝑖 = 〈(𝑛𝑂𝑇 . 𝑘1 . , … 𝑘𝑗 ) , (𝑛𝑂𝑁 . 𝑙1 . , … 𝑙𝑗 ) , (𝑛𝑂𝑀 . 𝑔1 . , … 𝑔𝑗 ) , 𝑚𝑖 〉 , 𝑖 = ̅̅̅̅̅
1, 𝑁, (3)
𝑖 𝑖 𝑖
where 𝒋 – ordinal number of the level of the corresponding topological taxonomic tree (𝑶𝑇 , 𝑶𝑁 , 𝑶𝑀 ,
𝑶𝑆 ), and 𝑛𝑂𝑇 . 𝑘1 . , … 𝑘𝑗 – node ordinal number of the topological taxonomic tree 𝑶𝑇 ; 𝑛𝑂𝑁 . 𝑙1 . , … 𝑙𝑗 –
node ordinal number of the taxonomic tree 𝑶𝑁 ; 𝑛𝑂𝑀 . 𝑔1 . , … 𝑔𝑗 – node ordinal number of the
taxonomic tree 𝑶𝑀 ; 𝑚𝑖 1, 5 – a natural number that can take values from 1 to 5 and characterizes
the manifestation degree of the disease (deviation from the norm, pathogenic factor) (in the absence of
deviations from the norm number 𝑚𝑖 = 0, larger value 𝑚𝑖 indicates a greater manifestation degree of
the disease). You can provide the following numerical scale interpretation of the disease manifestation
degree: 1 – very weak, 2 – weak, 3 – medium (moderate), 4 – strong, 5 – very strong. Figure 3 shows
an example of the formation of a diagnostic vector according to the above approach.
Figure 3, for example, presents simplified fragments of ontologies {𝑶𝑇 , 𝑶𝑁 , 𝑶𝑀 , 𝑶𝑆 }, and the
dashed line connects the nodes that were selected in the diagnostic process. Thus, the vector 𝑋 =
(𝑥1 , 𝑥2 , … , 𝑥𝑁 ) information diagnostic space 𝑿 Chinese image medicine for this example will be
displayed as 𝑋 = (1.1, 2.2.1, 3.3, 4.1).
Figure 3: Procedure for forming a diagnostic vector for CIM
In addition to the above information, there is a field in which the CIM-specialist can provide in
text format additional information that is not displayed by means of formal ontologies, or that clarifies
this information.
Thus, the diagnostic space 𝑿 contains both qualitative and quantitative characteristics (indicators),
which in their entirety holistically characterize the patient's condition by CIM methods. Qualitative
characteristics are contained in CIM ontologies, and quantitative ones determine the manifestation
degree of a disease and can be set on a certain numerical (for example, from 1 to 5) or non-numerical
(for example, very weak, weak, medium, strong, very strong) scale. Any vector (other than zero) from
space 𝑿 indicates the presence of a certain type of disease in the patient, which are detected by CIM
methods.
For the convenience of forming a diagnostic vector of the patient's condition by CIM methods it is
appropriate to develop an interactive visual environment based on an image model of the human body
and its parts, including physical, energy, informational aspects, as well as aspects of Yin-Yang,
Emptiness. Figure 4 shows one of the windows of this ontooriented system of professional healing
activities "Image Therapist", the core of which is the diagnostic and therapeutic ontologies of CIM.
Figure 4: Window with CIM-diagnostic information
3. Conclusions
The mathematical model of diagnostic space of CIM is constructed and the unified model of the
diagnosis is developed for their representation in the integrated onto-oriented information-analytical
environment. This mathematical model of diagnostic space contains both qualitative and quantitative
characteristics, which in their entirety holistically characterize the patient's condition by CIM
methods. A prototype of the Image Therapist system is described, in which are partially implemented
the described approaches about the diagnostic space and the model of diagnosis in CIM.
The developed model allows, along with the input of diagnostic information obtained by methods
of Western (official) medicine, to enter diagnostic information into the information system obtained
by CIM methods, which opens new possibilities for automated comparative analysis of different
diagnostic approaches in integrative scientific medicine.
4. References
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