Елена Владимировна Высоцкая, Маринэ Акоповна Георгиянц, Анна Ивановна Печерская, Андрей Павлович Порван, Наталья Николаевна Богуславская


Global climate changes and the increasing impact of mankind on nature have a stressful effect on biological objects. In the current situation, it is necessary to develop technologies for protecting biological objects and correcting their condition. In the presence of several alternative corrective facilities, arises the task of developing an information technology for choosing the optimal one for a given biological object in a particular situation. In this work, mathematical and methodological support of such technology is offered. The logical rules for each of the possible corrective facilities are formulated. To synthesize logical rules, it is suggested to use the method of forming a probabilistic conclusion. A context diagram and a first level decomposition diagram of the information technology for choosing the corrective facilities under stress impact on the biological object, which describes the input, output, control actions, functional information processes, data storage devices, external entities and the flow of data flows between them. The work of the proposed information technology is based on eight interrelated subprocesses. The information-logical model of data is constructed that reflects all objects and events, the information about which it is necessary to store, and the connections between them. Based on the developed information technology, the structure of the information system is proposed, which will allow to automatize the procedure of selecting corrective facilities. The structure of the system is five interrelated modules that perform the functions of the system. The strengths and weaknesses of the development have been identified. The opportunities of development and possible threats that may arise when implementing it are analyzed. The use of the developed information technology on the example of the choice of anesthetic support for a traumatologic operation will allow to automate the process of choosing an anesthetic support for a traumatological operation, facilitate the work of a doctor and improve the quality of medical care for patients


database; stress impact; regression model; perioperative period; information technology


Lebedinskiy, K. M. Anesteziya i sistemnaya gemodinamika [Anesthesia and systemic hemodynamics]. SPb, Chelovek Publ., 2000. 296 p.

Logvinenko, V. V., Shen, N. P. Sravnitelnaya harakteristika riskov razvitiya nezhelatelnyih sobyitiy i kriticheskih intsidentov pri obschey i regionarnoy anestezii. Analiz 6 let klinicheskoy praktiki [Comparative characteristics of the risks of development of undesirable events and critical incidents in general and regional anesthesia. Analysis of 6 years of clinical practice]. Regionarnaja anestezija i lechenie ostroj boli – Regional anesthesia and treatment of acute pain, 2015, no. 2, pp. 22–28.

Eroglu, A., Apan, A., Erturk, E., Ben-Shlomo, I. Comparison of the Anesthetic Techniques. Scientific World Journal, 2015, no. 2, pp. 50–84.

Dyagilev, M. A., Estrin, V. V., Kraynova, N. N. Sposob vyibora anesteziologicheskogo obespecheniya pri operativnyih vmeshatelstvah [Method of choosing anesthetics for surgery]. Available at: (аccessed 12.05.2018).

Logvinenko, V. V., Shen, N. P. Vyibor optimalnogo anesteziologicheskogo obespecheniya ambulatornyih operativnyih vmeshatelstv v travmatologii [The choice of optimal anesthesia for outpatient surgery in traumatology]. Regionarnaya anesteziya i lechenie ostroy boli – Regional anesthesia and treatment of acute pain, 2010, no 3, pp. 38–41.

Hendrickx, J., Lemmens, H., De Cooman, S., Van Zundert, A., Grouls, R., Mortier, E., De Wolf., A. Mathematical method to build an empirical model for inhaled anesthetic agent wash-in 2011. Available at: (аccessed 24.06.2011).

Porvan, A. Technology for determining of students adaptive capabilities. Information Technologies in Innovation Business Conference: Proceedings ITIB, 7-9 Oct 2015, Kharkiv, 2015, pp. 47–51.

Balym, Y., Vysotska, O., Pecherska, A., Bespalov, Y. Mathematical modeling of systemic colorometric parameters unmasking wild waterfowl. Eastern European Journal of Enterprise Technologies, 2017, no. 5(2-89), pp. 12–18.

Semenova, N. G., Kryilov, I. B. Razrabotka agentno-orientirovannoy intellektualnoy obuchayuschey sistemyi na osnove nechetkoy neyronnoy seti Takagi-Sugeno-Kanga [Development of agent-oriented intellectual learning system based on the fuzzy neural network Takagi-Sugeno-Kanga]. Vektor nauki TGU – Vector of science TSU, 2015, no. 2 (32-1), pp.11–19.

Truhanova, I. G., Baldin, I. N., Zaharova, N. O. Algoritm vyibora anesteziologicheskogo obespecheniya pri holetsistektomii u pozhilyih patsientov [Algorithm for choosing anesthesia for cholecystectomy in elderly patients] Izvestiya Samarskogo nauchnogo tsentra Rossiyskoy akademii nauk – Izvestiya of the Samara Scientific Center of the Russian Academy of Sciences, 2015, vol. 17, no. 2, pp. 392–397.

De Georgia, M. A., Kaffashi, F., Jacono, F. J., Loparo, K. A. Information Technology in Critical Care: Review of Monitoring and Data Acquisition Systems for Patient Care and Research. The Scientific World Journal, 2015, no. 9, pp. 9–15.

Makushev, V. O., NovIkova, A. O. Modelyuvannya fiziologiyi bolyu [Modeling the physiology of pain]. Biomeditsinskaya inzheneriya i elektronika – Biomedical Engineering and Electronics, 2015, no. 2(9), pp. 1–62.

Kruger, G. H., Chen, Ch., Blum, J. M., Shih, A. J., Tremper, K. K. Reactive software agent anesthesia decision support system. Systemics, cybernetics and informatics, 2011, vol. 9, no. 6, pp. 30–37.

Imhoff, M., Kuhls, S., Gather, U. Clinical relevance of alarms from patient monitors. Critical Care Medicine, 2007, no. 34, pp. 62–71.

Sokolskiy, V. M., Kantemirov, V. I. Рrocess control system of a multicomponent general anesthesia based on current measurement of physiological parameters. Modern problems of science and education, 2012, no. 1, pp. 14–26.

Maklarov, S. BPwin и Erwin. CASE-sredstva dlja razrabotki informacionnyh sistem [BPwin and Erwin. CASE-tools for the development of information systems]. Moscow, DIALOG - MIFI Publ., 2005, 256 p.

Chawda, M. N, Hildebrand, F., Pape, H. C., Giannoudis, P. V. Predicting outcome after multiple trauma: which scoring system? Injury, 2004, no. 35(4), pp. 347–358.

Georgiyants, М., Khvysyuk, О, Boguslavskaуa, N., Vysotska, О., Pecherska, А. Development of a mathematical model for predicting postoperative pain among patients with limb injuries. Eastern-European Journal of Enterprise Technologies, 2017, vol. 4, issue 4 (86), pp. 4 – 9.

Kuznetsov, M. MySQL 5. SPb, BHV-Peterburg Publ., 2010, 1007 p.



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