All articles by Gusev A. V.
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2020 № 4 Predictive analytics technologies in the management of the COVID‑19 pandemic
Recently, a new coronavirus infection, or COVID‑19, caused by the pathogen SARS-CoV‑2, has been continuing to
spread around the world rapidly. According to the World Health Organization (WHO), which declared this outbreak a pandemic, COVID‑19 is a serious public health problem of international concern. Due to the lack of proven effective treatment and vaccination against COVID‑19, precautions are considered by WHO to be strategic goals and a primary response to the pandemic. It is recommended that country guidelines adopt national health care programs aimed at assessing and reducing the risk of infection spread. Predictive analytics have begun to be actively used to compile population and personal forecasts of the progression of morbidity, mortality, assess the severity of the course of the disease, etc. This article provides an overview of available developments and publications on the use of predictive analytics in the management of COVID‑19 pandemic. -
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
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2017 № 2 Clinical Decisions Support in medical information systems of a medical organization.
In the article the review of various possibilities of support of acceptance of medical decisions in medical information systems of the medical organizations is presented. The description of functional requirements and prospects in terms of increasing the effectiveness of medical information systems in the informatization of clinical work of doctors.
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2018 № 2 Prospects for the further development of the medical statistics service through the transition to management based on data
In the article the problems of the system of collection of state statistical reporting existing in Russia and the consequences to which they result are analyzed. A review of literature and publications in the media and the blogosphere is provided, which reveal the existing shortcomings of medical statistics.
The gradual development of health management based on data with the refusal to use statistical reports is proposed. The key idea is a gradual refusal to apply the existing forms of state statistical reporting. Instead, it is necessary to create and consistently develop a system of support for management decisionmaking in the health sector, presented as one of the federal components of the EGIS. Ultimately, such a system should completely replace state statistical reporting approved by the orders of Rosstat, Ministry of Health, FFOMS and other executive authorities, including regional government bodies, as well as numerous disparate “monitoring”, “registers” and other federal management systems available in present time. The established single federal service EGISP should accumulate in itself all the formalized primary data, primarily depersonalized, from other components of the EGISP, such as medical information systems of medical organizations (MIS MO), regional services and EHISM systems, information systems of the TFOMS and etc. -
2018 № 4 Government software procurement and health information services in 2013–2017
The analysis of government procurement of information on health, as well as implementation services and technical support for the period 2013–2017. The analysis is based on data from monitoring sites state. procurement, which were conducted under the Federal Law No 44-FZ of 05.04.2013. The paper presents the main statistical indicators of competitive procedures, information about the winners of competitions and auctions
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2019 № 2 Trends and forecasts for the development of medical information systems in Russia