Background. A number of studies have been aimed at solving the problems of introducing information technology and systems, but the questions of informatization in rural medicine are not completely resolved. It is important to optimize the prognosis of diseases using available and inexpensive information methods for the improvement of primary healthcare. Objectives. The aim of our study was to develop an algorithm to optimize the decision-making prognosis of disease at the primary health care level based on information methods. Material and methods. The data used for analysis originated from the survey results of 63 patients with hypertension in educational and practical centers of primary health care (EPCPHC) of Ternopil region (Ukraine). For a deeper analysis and clustering, the neural network approach was used with the NeuroXL Classifier add-in application for Microsoft Excel. Results. Thirteen (19.40%) patients experienced health deterioration and the development of complications. It has been established that neural network clustering could effectively and objectively allocate patients to the appropriate category in terms of the average survey results. Cluster analysis results have shown that the combination of high blood pressure (systolic, diastolic and pulse) gave reason to anticipate the deterioration of patients' conditions. Conclusions. A decision algorithm was created in order to optimize the prediction of diseases at the primary health care level, and also to correct examination and treatment based on an analysis of average values of patients' examination and the use of neural network clustering.
The authors developed and substantiated the original methods of arterial oscillography, which were implemented in the developed Oranta-AO information system. The methods of application to the arterial oscillogram registered at measurement of arterial pressure gives the possibility to carry out the supplementary systematic assessment of health, functional state of cardiovascular system, its reserve possibilities etc. The authors also developed an Expert System (based on machine-learning methods) for the differential diagnosis of risks of heart, lung, mental illness and prognosis of some blood parameters. Oranta-AO software system was created based on research results due to methods and algorithms that were innovate. For the mathematical modeling of arterial oscillograms used cyclic random processes. Methods of arterial oscillograms processing based on its model in the form of a cyclic random process was developed. The method of evaluation of the rhythm function of arterial oscillograms and statistical methods for estimating the probabilistic characteristics of arterial oscillograms were developed. To solve the clustering problem, the Python
k
-means and
k
-means++ algorithm were used. Oranta-AO information system consists of three interrelated parts: mobile application, computing kernel and web system. Computing kernel and web system are deployed on AWS servers and have been tested already. The developed environment aims to be integrated into every new model of electronic meters in the world. Certification (EN 62304:2014, ISO 13485: 2018) in Ukraine is completed, PCT priority is completed. The next step will be to establish cooperation with manufacturers of electronic pressure monitors, patenting and certification in world.
The aim of study is finding complex pathological process markers occurred in COVID-19. Adaptive capacity, cardiovascular features, autonomic, central nervous systems in 67 patients with severe COVID-19 were studied and evaluated using (suggested by authors) temporal, spectral, correlation analysis of arterial oscillograms (AOG). The method is based on mathematical analysis adaptation of electrocardiographic signal heart rate variability to arterial pulsation variability analysis recorded during blood pressure measurement using an electronic tonometer VAT 41–2. Received results were compared with AOG 480 healthy (including 68 people after exercising) and 26 patients in a closed ward at psychoneurological hospital. Study results showed patients with severe COVID-19 have disorders at (four) cardiovascular system (CVS) regulation levels. It’s confirmed by lack of adequate sympathetic-adrenal response to a stressful situation due to severe COVID-19; higher than in healthy, parasympathetic part activity of autonomic nervous system. AOG spectral analysis revealed violation of management centralization, communication and coordination between CVS regulation levels. This leads to functional reserves decrease, low stress resistance of body and finally to a disease severe course and recovery processes. Arterial oscillography can be used to search markers of complex pathological processes occurred in COVID-19 and to improve methods of diagnosis, treatment, control of long-term results in clinical and family medicine.
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