Qualitatively new mechanisms for the analysis of blood cells and blood plasma proteins may open up the possibility of constructing a hematological data model (HDM) for early detection of a pathological process during the latent period of the disease. As such mechanisms, we use the algorithms of Sense Theory, new mathematics for artificial intelligence.
In this article, we use the results of a clinical blood test as a practical example.
1. Introduction
At the current time, of the main types of medical diagnoses formed by the method of their construction, which are often used in practice, two can be distinguished: a diagnosis by the therapeutic effect and a diagnosis by the result of the harmful effect of prescribed medications.
Most of these types of diagnostics are extremely dangerous and lead to negative consequences for human health. While such, the most high-quality types of diagnostics, such as differential or synthetic diagnosis, are less and less encountered in practice by the attending physician.
This is primarily due to the large amount of information that the practitioner must know and clearly and quickly operate on it already with the patient’s initial anamnesis.
“Dysfunction of internal organs and systems of the human body is always an inflammatory process.”
Inflammation as a typical pathological process develops according to general laws, regardless of the causes and localization caused by it. Immune, physiological and anatomical features of the human body leave an imprint on the course of the pathological process, however, its general hematological patterns remain.
The most informative medium about the state of the human body remains blood. The indicators of the blood system provide more than 70% of information about the patient’s condition and are extremely reactive to any physiological and biochemical changes inside the body.
For the transition from a symptomatic method of treatment, which is widespread at the current time throughout the world, which only drowns out the pain syndrome and does not cure the disease itself, to a hematological preventive method (HPM) that allows identifying its etiological factors already at the stage of the latent period of the disease, it is extremely necessary to develop an innovative model of over-large volumes of medical data, as well as tools for working with such a model.
The main characteristic of this model should be the ability to construct and analyze hematological patterns of uniform and plasma blood components. The presence of a large number of both newly constructed patterns and historically confirmed and stored in the system will allow to quickly identify an incipient pathological process and it's possible belonging to a certain disease, new or recurrent.
In our opinion, the Sense Theory [1] algorithms, which allow working in real-time mode with petabytes of data of various types, can become a very successful practical implementation in the construction of the above model.
2. Problem
The lack of a unified methodology for the dynamic construction of hematological patterns for the purpose of identifying pathological processes of diseases in the latent (incubation) period.
3. Solution
Below we will lay the foundation for the construction and practical implementation of a hematological preventive method (HPM) based on Sense Theory algorithms, using the example of a general clinical blood test.
Inflammation.
In the process of inflammation, the transition of plasma proteins and blood leukocytes from microcirculatory vessels to the focus of damage occurs. Damage, in turn, can be caused by various factors: biological, physical, chemical, psychogenic. The entire course of the inflammatory process is controlled mainly by endogenous chemicals [2], inflammatory mediators, which appear in the focus of inflammation.
The role of inflammatory mediators can be played by:
· lipids,
· peptides,
· proteins,
· monoamines,
· nucleosides,
· proteoglycans.
There are two main groups of inflammatory mediators. The first group of inflammatory mediators is formed before the damage. The second group forms immediately after damage. One of the main inflammatory mediators of the first group is histamine. Prostaglandins can be distinguished as mediators of the second group.
Histamine.
Histamine is a decarboxylation product of the amino acid histidine. Its main source is tissue mast cells and human blood basophils. Mast cell degranulation, histamine release, can be triggered by various stimuli:
· adenosine triphosphoric acid (ATA),
· heating/cooling,
· neuropeptides,
· complement fragments,
· E immunoglobulins.
The set of stimuli forms No-Sense Set [1]:
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