TIME SCALE BRANCHING METHOD FOR THE SITUATION DEVELOPMENT SIMULATION IN THE DIGITAL HEALTHCARE MONITORING SYSTEM 

Evgeniya A. Dodonova, Analyst, Open Code LLC (52/55 Ulyanovskaya street, Samara, Russia), E-mail: dodonova.evg@gmail.com
Irina N. Dubinina, Project manager, Open Code LLC (52/55 Ulyanovskaya street, Samara, Russia), E-mail: vartaric@yandex.ru
Oleg K. Golovnin, Candidate of technical sciences, associate professor, associate professor of the sub-department of information systems and technologies, Samara National Research University (34 Moskovskoe highway, Samara, Russia), E-mail: golovnin@bk.ru
Anton V. Ivaschenko, Doctor of technical sciences, professor, director of the Higher School of Medical Engineering, Samara State Medical University of the Ministry of Healthcare of the Russian Federation (89 Chapaevskaya street, Samara, Russia), E-mail: anton.ivashenko@gmail.com 

Background. The paper presents a new timeline branching method in monitoring and modeling systems, with which it is possible to determine the necessary and sufficient number of scenes for decision-making by dividing the situation under consideration into branches with positive, negative and neutral scenarios for the development of a complex social system, which makes it possible to increase the accuracy of the forecast. Materials and methods. The solution is based on the theory of cross-correlation analysis of odd time series, which allows you to process chains of events to determine linear and possible causal relationships. The timeline branching method consists of the following steps: branching, approximation and adaptive sampling of timelines, or a vector method for supply and demand. Results. The proposed method has been tested in order to analyze the dynamics of accumulated statistics on the key parameters "Number of new infections" and "Availability of medical care for patients with COVID". Conclusions. The proposed method makes it possible to identify critical scenes, which are important elements in planning, since can then be used to improve decision support process. 

digital transformation, social system, monitoring, risk management, time series, decision support 

Dodonova E.A., Dubinina I.N., Golovnin O.K., Ivashchenko A.V. Time scale branching method for the situation development simulation in the digital healthcare monitoring system. Modeli, sistemy, seti v ekonomike, tekhnike, prirode i obshchestve = Models, systems, networks in economics, technology, nature and society. 2023;(2):111–127. (In Russ.). doi: 10.21685/2227-8486-2023-2-7