Aim. To evaluate the clinical characteristics and frequency of prescribed anticoagulant therapy for patients with atrial fibrillation (AF) and heart failure (HF) in subjects of the Russian Federation based on a retrospective big data analysis using artificial intelligence technologies.Material and methods. For retrospective analysis, information was obtained from the Webiomed predictive analytics platform, which includes depersonalized data from electronic health records of outand/ or inpatients in 6 subjects of the Russian Federation, extracted using artificial intelligence technologies. From the database of patients with AF (n=144431), a group of individuals (n=20970) with an established diagnosis of HF and information on left ventricular ejection fraction (LVEF) was selected.Results. Patients with AF and HF (men, 43,7%; age 72,1±13,2 years; LVEF, 58,9±11,0%) had a history of smoking in 36,6% of cases, hypertension — in 86,7%, type 2 diabetes — in 26,6%, gout — in 2,7%, stage III and IV-V chronic kidney disease — in 50,9 and 15,6%, lower limb peripheral arterial disease — in 15,8%. The incidence of ischemic stroke, LV myocardial infarction and pulmonary embolism was 8,8, 14,7 and 2,4%, respectively. Anticoagulants, including direct oral ones, were administered to patients with AF and HF in 62,5% and 32,0% of cases, respectively. The frequency of their appointment did not significantly differ depending on LVEF.Conclusion. Patients with AF and HF are characterized by significant comorbidity, a higher incidence of cardiovascular events compared with the group of individuals with AF without HF, and an unsatisfactory percentage of anticoagulant therapy.

Objective: to review domestic and foreign literature on the issue of machine learning methods applied in medical information systems (MIS), to analyze the accuracy and efficiency of the technologies under study, their advantages and disadvantages, the possibilities of implementation in clinical practice.Material and methods. The literature search was performed in the PubMed/MEDLINE databases covering the period from 2000 to 2020 (using groups of keyphrases: "machine learning", "laboratory data", "clinical events", "prediction diseases"), CyberLeninka ("machine learning", "laboratory data", "clinical events", "prediction diseases" Russian keyphrases combinations) and Papers With Code ("clinical events", "prediction diseases", "electronic health record"). After reviewing the full text of 30 literature sources that met the selection criteria, the 19 most relevant articles were selected.Results. An analysis of sources that describe the application of artificial intelligence techniques to obtain predictive analytics, taking into account information about patients, such as demographic, anamnestic, and laboratory data, the data of instrumental studies, information about existing and former diseases available in MIS, was performed. The existing ways of predicting adverse medical outcomes using machine learning methods were considered. Information about the significance of the used laboratory data for constructing high-precision predictive mathematical models is presented.Conclusion. Implementation of machine learning algorithms in MIS seems to be a promising tool for effective prediction of adverse medical events for wide application in real clinical practice. It corresponds to the global trend in the development of personalized medicine based on the calculation of individual risk. There is an increase in the activity of research in the field of predicting noncommunicable diseases using artificial intelligence technologies.

National health systems are experiencing a number of common global challenges: population growth, demographic ageing, the prevalence of chronic noncommunicable diseases, the constant growth of health-care costs, while increasing resource scarcity, including staffing. At the same time, the rapid development of information technologies, including the accumulation of big data, artificial intelligence, telemedicine, remote patient monitoring, and the increasing availability of high-performance mobile devices and a high-speed Internet connection create truly unique prospects for the development of digital healthcare products and services. Growth of investment from tech giants and venture capital funds is one of the main drivers of digital healthcare transformation. More and more innovative products are being offered not to enhance the effectiveness of existing processes within health systems, but to create new alternative ways to receive medical care or reduce problems in its delivery. Thus, a key customer and a user of digital healthcare products is gradually becoming not the heads of medical organizations, public health authorities or doctors, but patients themselves. In this article, we present an analysis of the existing global trends and directions of development of the digital healthcare market, form our image of the future and the most prospective scenarios and technologies for products and services in this area.

The increase in the prevalence of cardiovascular diseases (CVDs) specifies the importance of their prediction, the need for accurate risk stratification, preventive and treatment interventions. Large medical databases and technologies for their processing in the form of machine learning algorithms that have appeared in recent years have the potential to improve predictive accuracy and personalize treatment approaches to CVDs. The review examines the application of machine learning in predicting and identifying cardiovascular events. The role of this technology both in the calculation of total cardiovascular risk and in the prediction of individual diseases and events is discussed. We compared the predictive accuracy of current risk scores and various machine learning algorithms. The conditions for using machine learning and developing personalized tactics for managing patients with CVDs are analyzed.

Background. Prediction of the new coronavirus infection (COVID-19) spread is important to take timely measures and initiate systemic preventive and anti-epidemic actions both at the regional and state levels to reduce morbidity and mortality.Objective: to develop a model for short-term forecasting of COVID-19 cases and deaths in the Russian Federation.Material and methods. The data for the model training were collected from the Stopcoronavirus.rf and Johns Hopkins University portals. It included 13 features to assess the infection dynamics and mortality, as well as the rate of morbidity and mortality in different countries and certain regions of the Russian Federation. The model was trained by the CatBoost gradient boosting method and retrained daily with updated data.Results. The forecast model of COVID-19 cases and deaths for the period of up to 14 days was created. The Mean Absolute Percentage Error (MAPE) estimate of the model’s accuracy ranged from 2.3% to 24% for 85 regions of the Russian Federation. The advantage of the CatBoost machine learning method over linear regression was shown using the example of the Root Mean Square Error (RMSE) value. The model showed less error for regions with a large population than for less populated ones.Conclusion. The model can be used not only to predict of the pandemic of the novel coronavirus infection but also to control and assess the spread of diseases from the group of new infections at their emergence, peak incidence, and stabilization period.