Artificial intelligence in health care
  • 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.

    Authors: Gusev A. V. [8] Novitsky R. E. [3]

    Tags: artificial intelligence10 covid-194 dashboard2 machine learning7 predictive analytics1 software2

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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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  • Special opinion
  • 2019 № 2 Trends and forecasts for the development of medical information systems in Russia


    Authors: Gusev A. V. [8] Pliss M. A. [2] Levin M. B. [1] Novitsky R. E. [3]

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