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. [7] Pliss M. A. [2]

    Tags: artificial intelligence6 healthcare7 machine learning5 medicine5 neural networks7

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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. [7]

    Tags: artificial intelligence6 healthcare7 machine learning5 medicine5 neural networks7

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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. [7] Novitsky R. E. [2] Gavrilov D. V. [1] Korsakov I. N. [1] Serova L. M. [1] Kuznetsova T. Yu. [1]

    Tags: ai1 artificial intelligence6 cardiovascular diseases2 cvd1 determining the risk of developing cardiovascular diseases1 healthcare7 machine learning5 medicine5 ml1 risk factors4

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  • 2018 № 4 Black box problem overcoming in medical applications of machine learning

    A new interface for machine learning predicting models is proposed. Approach is based on optimal valid partitioning (OVP) technique and the modified method of statistically weighted syndromes (LSWR). The interface allows you to overcome the problem of “black box” illustrating prediction process by scatter plots, ROC curves and informative indicators ranking

    Authors: Kuznetsova A. V. [2] Senko. O. V. [2] Kuznetsova Ju. O. [1]

    Tags: forecasting2 machine learning5

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  • 2017 № 2 Determination of risk factors of cardiovascular mortality in institutions of the penitentiary system using the machine learning methods.

    The article presents the results of the first clinical and epidemiological studies to identify risk factors for fatal cardiovascular disease in patients of medical institutions of the penitentiary system. In a research the Machine learning methods based on creation of optimal partitioning signs space and recognition methods were applied. It is with high reliability has allowed to determine predictive factors of hospital mortality in cardiac patients, which became: the use of a strong tonic drink «chifir», age, weight, growth, systolic and diastolic blood pressure, hemoglobin level, heart rate, left ventricular ejection fraction, end-systolic and end-diastolic left-ventricular dimension, hypertension and the number of criminal record.

    Authors: Dyuzheva E. V. [2] Kuznetsova A. V. [2] Senko. O. V. [2]

    Tags: cardiovascular disease1 machine learning5 methods of optimal partitioning1 recognition1 the penitentiary system1

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