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.
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.
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
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.