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. Mathematical modeling, diagnostic space, ontology, information system, Chinese image imaging medicine.
medicine, integrative scientific medicine.
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 [
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 [
2020 Copyright for this paper by its authors.
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 [
The developing purpose an information-analytical environment for CIM, the generalized
architecture of which is presented in Fig.1 [
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.
(age, gender, family members, etc.)
information obtained by methods of conventional (Western) medicine, namely, medical history and results of medical examinations (laboratory tests, results of functional diagnostics, doctor's report, etc.)
methods, namely, the results of diagnosis by TCM (examination, listening, palpation diagnosis results), the results of energy diagnosis by hand and (or) body, the results of internal imaging ("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 CIM should be preceded by the development of a diagnostic ontology CIM (ontology of the theory and technologies of diagnosing in CIM), which as its subontology should include nosological ontology CIM, topological diagnostic ontology CIM, and ontology methods of obtaining and specifying sensory-image diagnostic information (diagnostic methods ontology ) in CIM, as well as an ontology 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: = { , , , }, (1)
Topological diagnostic ontology CIM displays information about the topological localization of diseases. Nosological ontology CIM reflects knowledge of the diseases types (classes) that are accepted in the diagnostic theory of CIM.
Ontology 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 [
Information on the components of the diagnostic ontology of CIM is grouped in table 2.
(classes) of diseases which are accepted in the CIM diagnostic theory Topological diagnostic ontology CIM reflects information on the 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.
sensory-image diagnostic information 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 (∙) from the ontologies described above. In this case, the CIM diagnostic space is a function of the ontologies , , , , namely: = { , , , }, (2) where (∙) – a certain type of method (algorithm, procedure) formation of the diagnosis in CIM from the corresponding ontologies , , , .
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 0 from 1 to 4. For example, to ontology is assigned number 1 ( 0 = 1), to ontology is assigned number 2 ( 0 = 2), to ontology is assigned number 3, and to ontology 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 , -th level of a taxonomic tree with a serial number 0 is presented as a combination of numbers separated by a period n0. 1. , … , where the first component displays the ordinal number of the taxonomic tree, and 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.
Based on the above, we create a procedure for forming a vector = ( 1, 2, … , ) information diagnostic space Chinese image medicine, which is a unified formalized representation of the diagnosis by CIM methods. Each −th component of vector = ( 1, 2, … , ) 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 components of the diagnostic vector = ( 1, 2, … , ) 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, −th component of vector = ( 1, 2, … , ) is four formal objects: = 〈( . 1. , … ) , ( . 1. , … ) , ( . 1. , … ) , 〉 , = ̅1̅,̅̅̅, (3) where – ordinal number of the level of the corresponding topological taxonomic tree ( , , , ), and . 1. , … – node ordinal number of the topological taxonomic tree ; . 1. , … – node ordinal number of the taxonomic tree ; . 1. , … – node ordinal number of the taxonomic tree ; 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 = 0, larger value 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 { , , , }, and the dashed line connects the nodes that were selected in the diagnostic process. Thus, the vector = ( 1, 2, … , ) information diagnostic space Chinese image medicine for this example will be displayed as = (1.1, 2.2.1, 3.3, 4.1).
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 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 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.
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.