2017 № 3 Organizational telemedicine
Scientific bases of organizational telemedicine as a new direction of application of information and telecom- munication technologies in the system of citizens’ health protection are developed. The terminology, principles and role of telemedicine in the field of organization of public health and public health are defined. The advantages of using organizational telemedicine for improving the organization of medical care in the system of national and departmental health of the Russian Federation are shown. A new view of telemedicine as a tool for maneuvering health resources is proposed for the rapid change in the volume and level of medical care in the interests of a broad contingent of the population. The integrative role of organizational telemedicine in the integrated application of various information technologies in health care is shown
2019 № 1 Maximus Intellectual Information and Analytical System for Medicine and Healthcare
This article discusses the relevance, purpose, functions and capabilities of intelligent information and analytical modules of diagnostic and treatment processes management systems, on the base of the use of Maximus system in the regional network of clinics of Nefrosovet group, as well as in relation to medicine and healthcare in Russia as a whole. It justifies the importance of automating the data collection, starting from the diagnosis and treatment process to the middle and upper levels of analysis and reporting. Article presents a line of business intelligence software services and decision support systems – process control center, visual analyzer of DTP parameters and reporting system, analytical patient registry, diagnostic machine, causal patient model, diagnostic and treatment programs, etc., using the nephrology service with hemodialysis, treatment of complications and associated diseases as an example.
The architecture of presented system is service oriented and multiplatform. Article also describes the structures of Maximus BI and DSS subsystems, as well as the architecture of one of the main services – diagnostic and treatment programs. Article shows modern information technologies for managing business processes used in the system, as well as selected user interfaces and solutions.
2015 № 1 Use of Open UMS format for document flow formalization in medicine.
The question about construction of medical documents by means of AURRORA MIS with the use of the Open UMS format is considered in the work. The approach suggested allows data storage in the electronic form suitable for generation of required statistical reports and different researches and preserves a possibility of correct data interpretation.
2017 № 3 Reengineering of public health system, based on a person-centered model, hybrid project management approaches and methods of artificial intelligence
The paper considers new approaches of public health emerging on the platform of the technological revolution of information systems. The authors describe the key methods of managerial, technological and mathe- matical interaction with the collective information infrastructure of the healthcare system that will bring qualitative changes in the near future and will form the basis of a digital society, digital economy and public health of a new type. The methods are considered from the point of view of their influence on the formation of new approaches in the process of the economic and social transformation of waves of innovation. The model of the person-centered health care system and the forecast of its impact on the subject area are presented, the advantages of the new model are summarized and the agile project management approaches to its implementation in the current stage of development are presented.
2020 № 2 Artificial intelligence technologies in medicine and healthcare: Russia’s position on the global patent and publication landscape
An overview of public policy measures aimed at the development of artificial intelligence (AI) technologies in the world and in Russia is presented. In order to evaluate the competitiveness of domestic developments created for the use of AI technologies in medicine and healthcare, a scientometric and patent analysis of the direction for the period 2010–2019 was performed. Based on the analysis of research fronts using the Essential science indicators methodology, the most promising research strategies have been identified. It is shown that on the global publishing landscape, Russia occupies the 27th position in the world by the number of publications devoted to the use of AI in healthcare: Russian researchers account for less than 1% of publications indexed in the Web of science. To enter the top 5 countries in terms of publication activity in this thematic cluster, Russia needs to increase the number of publications by more than 6 times. Of the 16 companies in whose publications the participation of Russian authors are indicated, 13 are foreign. In General, only 14% of publications in the thematic category “Computer science, artificial intelligence” were made in collaboration with the industrial sector. In the landscape formed by patent documents that protect technical solutions in the field of AI in medicine, Russia takes positions that do not confirm its intention to fight for promising markets for goods and services created on the basis of these technologies. In the field of medical AI developments, the number of Russian patents issued to non-residents of the country significantly exceeds the number of holders of domestic patents. Only 12 patents of Russian developers оn AI technologies for healthcare issued by foreign patent offices were found.
2019 № 3 Prospects for the using of machine learning methods for predicting cardiovascular disease
Morbidity and mortality from cardiovascular diseases (CVD) has remained the leading rate in recent decades worldwide. Primary prevention methods based on the management of cardiovascular risk factors are most effective in reducing the burden of CVD. In preventive medicine for risk management of CVD use the riskometers – scales that was obtained as a result of long prospective studies.But the practical application of the developing scales has showed the limitations in the forecast accuracy. Machine learning makes it possible to improve the accuracy of cardiovascular risk prediction due to nonlinear relationships of their deeper adjustment between risk factors and disease outcomes.
2236 patients’ data were used. We trained the model on the features used in the Framingham scale construction. We compared the resulting model and the Framingham scale for the accuracy of the cardiovascular event prediction. Thus, according to the ROC analysis for the Framingham scale, the indicators are as follows: precision Accuracy: 70,0%, the AUC: 0.59. At the same time for the model obtained using machine learning similar indicators were: Accuracy: 78,8%, AUC: 0.84. Thus, the use of machine learning algorithms including deeplearning algorithms can significantly improve the accuracy of cardiovascular risk prediction of trained models.
2018 № 3 The basic recommendations for the creation and development of information systems in health care based on artificial intelligence
Artificial intelligence is becoming one of the main drivers in solving serious problems of medicine and health, such as inadequate resources, further improving efficiency, quality and speed of work. All over the world, more and more solutions are being developed in this area. However, the more new products appear, the more questions and problems arise.
The work analyzes some foreign publications and research results, which studied the main problems associated with the creation and implementation of artificial intelligence in health care. As a result of the analysis, a number of practical recommendations were formulated that will help increase the likelihood of successful creation and introduction of such products in the practical link of health.
2017 № 3 Prospects for neural networks and deep machine learning in creating health solutions
The paper gives an overview of the prospects of using neural networks and deep machine learning in the creation of artificial intelligence systems for healthcare. The definition and explanations on the technologies of machine learning and neural networks are given. The review of already implemented artificial intelligence projects is presented, as well as the forecast of the most promising directions of development in the near future