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According to the Concept of Ukraine`s Digital Economy and Society Development in 2018-2020, the key components of "digitalization" are the development of digital infrastructure-broadband Internet throughout Ukraine, and the promotion of digital transformations in various sectors of the economy and society, including medicine. The purpose of the paper is to analyze the stages of digital transformation in medicine and the results of authors and their colleagues of the MIS department for the development of information technologies of digital medicine. Results. A generated model of digital transformation in medicine is presented and several main stages of this transformation are highlighted: І-digital transformation of primary medical information; ІІ-development of support systems for the diagnostic and treatment process; ІІІ-development of technologies and systems for supporting the physicians` activities with digital information; IV-mobile medicine; V-the digital medicine

In the Big Data era, decision tree methods, machine learning, and neural networks, along with other Data Mining methods became an alternative to classical statistical methods as a more useful tool for analyzing large and inhomogeneous data. Neural Networks methods have emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction, treatment.The purpose of the paper is to indentificate the control parameters of the surgical intervention to optimize the EF ejection fraction after the surgery using a Data Mining method (neural network) models.Results. The analysis of changes in hemodynamic parameters of children with severe heart defects due to surgery -implantation of conduit. Changes in these parameters after surgery were analyzed using analysis of variance for repeated measurements (RepANOVA).

Objectives: Juvenile idiopathic arthritis is a chronic disease that affects the synovial membrane of the joints, but can also lead to secondary lesions of the cardiovascular system. The most important mechanism of myocardial damage is associated with the effect of proinflammatory cytokines. The aim of the study was to propose a method of early detection of cardiovascular system changes and lesions in patients with juvenile idiopathic arthritis based on the determination of allelic polymorphism combined with electrocardiography data. Materials and methods: 102 patients with juvenile idiopathic arthritis underwent a general clinical examination. The overall activity of juvenile idiopathic arthritis was assessed using the Juvenile Arthritis Disease Activity Score. In addition, the patients underwent an electrocardiographic evaluation using the software and hardware complex “Cardioplus P,” which is a portable electrocardiograph providing “signal-averaged” electrocardiography performing the recognition and measurement of amplitude-time parameters, and calculation of secondary electrocardiography parameters. The genotypes of patients were additionally determined by alleles of the TNF-α (G308A) and IL6 (G174C) genes by polymerase chain reaction. Results: The overall number of mutations affects the course of the disease, with two or more mutations being associated with a more aggressive course of the disease, a more pronounced degree of inflammation, and a higher frequency of extra-articular lesions. The complex indicator of the functional state of the myocardium according to the electrocardiography data differed significantly (p = 0.00001) in clusters. Conclusion: Patients with juvenile idiopathic arthritis with two or more mutations in different genes of proinflammatory cytokines have a higher activity of the inflammatory process and a higher frequency of cardiovascular changes according to 4th generation electrocardiography. The determination of polymorphism may be useful in evaluating the risk of development of cardiovascular system abnormalities.