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.
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
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.
2019 № 3 Prospects for the use 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.
2019 № 3 Experience in developing and implementing an oncological formations searching system using artificial intelligence with the example of X‑ray computed tomography of the lungs
The experience of creating and implementing AI system Botkin.AI for identifying pulmonary nodules according to CT
data is considered. The main parameters of the model are described, the results of pilot projects of platform practical application in several regions of the Russian Federation are presented. Examples of a platform application for identifying pulmonary nodules with various size and localization are given. During pilot projects in the regions, 7 patients with high suspicion of pulmonary malignancy were identified. The results and experience show that the applying of the Botkin.AI system can be used both for the implementation of regional lung cancer screening programs and as an additional tool to increase the lung cancer detections with introducing automatic revision of chest CT data, regardless of these studies indications.
2017 № 4 Experience of the Development of the Software Package for Neural Network Diagnosis and Prediction of Diseases of Hepatopancreatoduodenal Zone
The article presents the experience of the internal development of a software package for diagnosis and forecasting diseases of hepatopancreatoduodenal zone based on the artificial neural network of multilayer perceptron type with hyperbol- ic tangent taken as an activation function. The article includes the characteristics of the analyzed data which is the set of risk factors for the development of peptic ulcer, cholecystitis and pancreatitis and substantiates the necessity for the application of automated control systems acting on the principles of artificial neural networks. The methods of operating of a multilayer per- ceptron are given, and there are proposed modifications intended to optimize the development of the software package and to solve a number of problems that arise during practical implementation of the system and during data preparation. A set of possible input and output parameters of the network, intended for its training, is proposed. The article contains the description of the practically developed user interface, intended to create, configure, train and clinically apply the artificial neural network, as well as to construct its graphs and statistically control its functioning.