Many diagnoses in various areas of medicine and biomedicine are often complex, expensive, and involve a long sequence of steps. These steps can be highly complex, compromising diagnostic performance, especially in remote regions of developing countries.
Quick access to patient information and their exams, in an organized, decentralized, and distributed manner, is very important for the medical community in general. The use of this data by Diagnostic Support Systems can increase the accuracy of the result.
The development of computational models based on the perspective of the specialist physician aims to transform clinical information into knowledge, to propose the creation of more efficient diagnostic support systems. This definition aims to facilitate the data analysis process and allow the formalization of information related to each diagnosis. The computational concepts used to create this technology are linked to Knowledge Engineering.
With the automated analysis of information banks (clinical data, laboratory tests, imaging tests, and diagnoses), the aim is to establish the degree of relationship of a specific diagnostic context to a given patient and their respective symptoms. In strict terms, knowledge is only created by individuals, and organizations must support creative individuals or provide them with appropriate contexts. This process should be understood as a factor for organizationally expanding the knowledge created by individuals, crystallizing it as part of the network within the institution. It is also noted that this process occurs within an expanding community of interaction, which crosses inter-organizational levels and boundaries. Ontology can be defined as an abstract and simplified explicit specification of a domain to be represented. In the area of Intelligent Systems, if an entity can be represented, then it is an explicit specification of a conceptualization. Thus, conceptualization is defined as the structured interpretation of concepts and, relating to them. In this case, it refers to those selected as relevant in a given domain. The term “explicit” refers to the set of these terms used and their restrictions are previously and explicitly defined. The importance of formalizing an ontology refers to the ability to process it by the computer, which excludes definitions in natural language. Its sharing is justified because it describes consensual knowledge, which is used by more than one individual and accepted by a group. Thus, an ontological structure is the union of a non-empty set of attributes, concepts, and specifications in contrast to hierarchies, with the basic function of representing the domain through relationships and axioms.
The level of knowledge provides the means to "rationalize" the behavior of a system from the point of view of an external observer; formalization is established through an agent that has the intelligence to achieve its objectives. The agent can execute a set of actions and choose them according to the principle of rationality. A model at this level of knowledge, however, is not only composed of the domain that the agent appears to be using but, more importantly, is the structure within which this knowledge is being used to achieve the objectives.
A diagnostic support system basically has two aspects that influence its effectiveness. The first is associated with the information processing methodology, and the second with the presentation of results through intuitive interaction with the user. The graph.md platform believes that a single environment, which is primarily ergonomic, would allow the exploration of all related data within a large hybrid universe, with images of different types and textual or even structured information, which would allow research in different sources of information, support for diagnosis and/or health research. [diagnosis e1 audiobook]
