Regional informatizatian projects
  • 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.

    Authors: Novitskiy V. O. [4] Galchenkov A. S. [2] Malkoch A. V. [2] Chemeris A. N. [1]

    Tags: analytical registry1 application server1 bi2 business process1 causal patient model1 clinic1 control1 data mining5 database2 datamart1 dbms1 diagnostic and treatment process4 diagnostic machine1 dss3 efficiency4 formalization1 healthcare8 hemodialysis2 intellectual information and analytical system1 knowledge base4 maximus1 medicine7 national clinical guidelines1 nefrosovet1 nephrology2 process control center1 reporting1 software module1 storage1 visual analyzer1 web-service1 widget1

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  • Digital healthcare
  • 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.

    Authors: Mukhin Yu. Yu. [1] Mukhin K. Yu. [1]

    Tags: agile1 agile project management approaches1 artificial intelligence10 business-agility1 digital economy3 healthcare8 innovations & innovation management1 medicine7 neural networks9 project management1

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  • Decision support systems
  • 2020 № 1 Life cycle of decision support systems as medical technologies

    Decision support systems (DSS) in medicine can be classified into reference and intellectual, and the latter, in turn, into
    modeling and imitating human reasoning. Modeling systems are based on formalized expert knowledge, and imitating ones are
    based on models built by various multidimensional data analysis methods. DSS should be considered as medical technologies,
    therefore, after their development, assessing of analytical (technical) and clinical validity should follow, regardless of current
    national regulatory documents. Clinical validation have to be based on principles of evidence based medicine and demonstrate
    superiority, non-inferiority or equivalence to routine practice. Then a clinical and economic analysis can be carried out in order
    to justify the economic feasibility of DSS, and later health technology assessment can be performed.

    Authors: O. Yu. Rebrova [2]

    Tags: analytical validation1 clinical economic analysis1 clinical validation1 decision support system3 life cycle1 medicine7

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  • Automated analytics in healthcare
  • 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.

    Authors: Tsvetkova L. A. [4] Cherchenko. О. V. [2] Kurakova N. G. [5]

    Tags: artificial intelligence10 healthcare8 medicine7 patent activity2 public  policy  measures1 publication  activity1 research  fronts1

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  • Artificial intelligence in health care
  • 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.

    Authors: Gusev A. V. [8] Pliss M. A. [2]

    Tags: artificial intelligence10 healthcare8 machine learning7 medicine7 neural networks9

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  • 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

    Authors: Gusev A. V. [8]

    Tags: artificial intelligence10 healthcare8 machine learning7 medicine7 neural networks9

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  • 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.

    Authors: Gusev A. V. [8] Novitsky R. E. [3] Gavrilov D. V. [2] Korsakov I. N. [1] Serova L. M. [2] Kuznetsova T. Yu. [1]

    Tags: ai1 artificial intelligence10 cardiovascular diseases2 cvd1 determining the risk of developing cardiovascular diseases1 healthcare8 machine learning7 medicine7 ml1 risk factors5

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