Figurative comparisons and eponims in functional diagnostics. Part 4. Other methods of functional diagnostic

This article is the fourth of a series of materials that tell about figurative comparisons and eponyms in modern functional diagnostics. Daily ECG monitoring, ECG treadmill test, daily blood pressure monitoring, spirometry, cardiorespiratory monitoring were considered. The names of great scientists who have made a great contribution to the history of medicine are given: Holter, Bruce — ‘father of the cardiology of exercises’, Tiffeneau, Gensler. Figurative comparisons like ‘electric storm’, ‘white coat hypertension’, ‘shark’s tooth’, ‘fat guy ‘Joe’ are described. The terms that will be discussed in the article have not only scientific, but also applied meaning. The article will be useful and interesting to students of medical universities, residents and doctors, whom it will help to check and, possibly, update their knowledge.

The organization of the functional diagnostics service in the Federal State Budgetary Institution Main Military Clinical Hospital named after аcademician N.N. Burdenko

Modern functional diagnostics (FD) studies the functions of organs and systems of the human body using instrumental research methods and quantitative analysis. The Functional Diagnostics Service in the Armed Forces of the Russian Federation has a long tradition and is actively developing now. The purpose of the work. To analyze the organization of the functional diagnostics service in the Federal State Budgetary Institution «Main Military Clinical Hospital named after Academician N.N. Burdenko» of the Ministry of Defense of the Russian Federation in modern conditions. Results and discussion. The existing system of FD organization in the hospital is analyzed, as well as whether the organizational and staff structure of the FD units, their placement and equipment meet the tasks taken on by the hospital. The features of the organization and conduct of functional diagnostic studies in the context of the COVID-19 pandemic are considered. The digital transformation in the field of FD is discussed. Conclusion. The created system of organizing and performing functional diagnostics in the hospital allows us to solve the tasks taken on by the hospital at high quality standards and to react quickly to changes in the situation.

Functional-diagnostic approach to the system of education of sportsmanship of swimmers

The problem of athletes' working capacity recovery after training and competitive loads is one of the most urgent in sports. The results of our research, carried out in the process of year- round training of members of the national swimming teams of the legal and polytechnic universities, showed that one of the systems experiencing the greatest stress and changes under the influence of a training load is the neuromuscular apparatus. The purpose of the work was to organize the restoration of sports performance, which is based on a functional diagnostic approach. The proposed system of restoration of sports performance, based on the constant consideration of the functional state of the swimmers' organism, allows to ensure adequate use of rehabilitation means, high individualization and effectiveness of the impact.

THE ROLE OF PHYSIOTHERAPY AND ASSOCIATED METHODS IN THE ATHLETE RECOVERY

In the process of sports training, the effort-recovery relationship is a considerable theme to approach. Achieving performance is only possible if, in parallel with the requirements regarding the volume and intensity of the effort, action is taken through methods and measures of recovery, of restoring the biological potential of the athlete. Basketball is a very widespread and appreciated team sport.We assume that the recovery of the athlete can be achieved with the help of physical therapy and its associated methods by combating the generalized fatigue after training at the right time and by the most effective techniques for a quick reintegration of the athlete in the competitive circuit.The research took place on a group of 5 players of the county men's basketball team, in the period of January-March of 2020, în Iași, Romania. For each subject, a record sheet of functional diagnostic data (joint balance, muscle balance, evaluation of painful points but also personalized tests) was completed, both initially and finally.We used goniometry as a method of measuring the amplitudes of movement in the joint, muscle testing to determine muscle strength, comparative measurements of muscle perimeters (knees, legs, thighs) and personalized tests adapted to each athlete,depending on the information received from them. All athletes recorded a significant decrease of the initial pain, a very good increase of mobility in joint amplitude compared to the initial amplitude of the painfull segment and a improvement of muscle strength at maximum capacity.

ІНФОРМАЦІЙНО-ЕКСТРЕМАЛЬНИЙ КЛАСТЕР-АНАЛІЗ ВХІДНИХ ДАНИХ ПРИ ФУНКЦІОНАЛЬНОМУ ДІАГНОСТУВАННІ

The study aims to increase the functional efficiency of machine learning of the functional diagnosis system of a multi-rope shaft hoist through cluster analysis of diagnostic features. To achieve the goal, it was necessary to solve the following tasks: formalize the formulation of the task of information synthesis, capable of learning a functional diagnosis system, which operates in the cluster-analysis mode of diagnostic signs; to propose a categorical model and, on its basis, to develop an algorithm for information-extreme cluster analysis of diagnostic signs in the process of information-extreme machine learning of a functional diagnostic system; carry out fuzzification of input fuzzy data by optimizing the geometric parameters of hyperspherical containers of recognition classes that characterize the possible technical conditions of the diagnostic object; to develop an algorithm and implement it on the example of information synthesis of the functional diagnostics system of a multi-rope mine hoisting machine. The object of the study is the processes of information synthesis of a functional diagnostic system capable of learning, integrated into the automated control system of a multi-rope mine hoisting machine. The subject of the study is categorical models, an information-extremal machine learning algorithm of a functional diagnostic system that operates in the cluster analysis model of diagnostic signs and constructs decision rules. The research methods are based on the ideas and methods of information-extreme intellectual data analysis technology, a theoretical-informational approach to assessing the functional effectiveness of machine learning and on the geometric approach of pattern recognition theory. As a result, the following results were obtained: a categorical model was proposed, and on its basis, an algorithm for information-extremal machine learning of the functional diagnostics system for a multi-rope mine hoist was developed and implemented, which allows you to automatically generate an input classified fuzzy training matrix, which significantly reduces time and material costs when creating incoming mathematical description. The obtained result was achieved by cluster analysis of structured vectors of diagnostic signs obtained from archival data for three recognition classes using the k-means procedure. As a criterion for optimizing machine learning parameters, we considered a modified Kullback measure in the form of a functional on the exact characteristics of diagnostic solutions and distance criteria for the proximity of recognition classes. Based on the optimal geometric parameters of the containers of recognition classes obtained during machine learning, decisive rules were constructed that allowed us to classify the vectors of diagnostic features of recognition classes with a rather high total probability of making the correct diagnostic decisions. Conclusions. The scientific novelty of the results obtained consists in the development of a new method for the information synthesis of the functional diagnostics system of a multi-rope mine hoisting machine, which operates in the cluster analysis model, which made it possible to automatically form an input classified fuzzy training matrix with its subsequent dephasification in the process of information-extreme machine learning system.