Decision support system for determining the dosage of medications in the treatment technology of preeclampsia of pregnant women

The problem of preeclampsia is one of the urgent in modern obstetrics, since this disease is the most common and serious complication of pregnancy, and the problem of treating severe forms of preeclampsia is one of the most difficult in obstetric anesthesiology and resuscitation. The high mortality rate is based on the lack of accurate knowledge about the pathogenesis of the disease, which depends on many factors, diagnostic criteria, which leads to inadequate therapy and various complications, depending on the timeliness and method of delivery, the volume of anesthetic and resuscitation care. Therefore, the study of methods for constructing automated and expert systems using modern methods of artificial intelligence and allowing to increase the effectiveness of the treatment of preeclampsia of pregnant women is relevant. This article discusses the development of a decision support system for determining the dosage of medications in the treatment technology of preeclampsia of pregnant women based on membership functions of several arguments. As a result of experimental tests, it was found that the relative deviation of the dosages calculated by the decision support system from the dosages established in the comparative tests by a qualified doctor does not exceed five percent. At the same time, the use of the results of the work made it possible to increase the number of severe patients served by one resuscitation doctor by at least two times, by reducing the time to establish a diagnosis.

1. Sanchez E. et al. Linguistic approach in fuzzy logic of W. H. O. classification of dyslipoproteinemias. Fuzzy set and theoryrecent development. Yager ed. Pergamon, 1982.

2. Adlassing K.P. Fuzzy set theory in medical diagnosis. IЕЕЕ Trans. Vol. SMC-16;2:260- 265.

3. Tazaki E. et al. Development of automated health testing and services system via fuzzy reasoning. Proc. IEEE Inc. Conf. on SMC. 1986:342-346.

4. Asai K., Vatada D., Iwai S., et al. Applied Fuzzy Systems: transl. from Japan. Moscow: Mir, 1993.

5. Khromushin V.A., Panshina M.V., Dahilnev V.I., Kitanina K.Yu, Khromushin O.V. Building an expert system based on an algebraic model of constructive logic using the example of gestosis. Bulletin of new medical technologies. 2013; 1; publication 1-1. Available at: http://www.medtsu.tula.ru/VNMT/Bulletin/E2013-l/ExpSys.pdf.

6. Khromushin V.A. Comparative analysis of the algebraic model of constructive logic. Bulletin of new medical technologies. 2013; 1; publication 1-19. Available at: http://www.medtsu.tula.ru/VNMT/Bulletin/E2013-l/4500.pdf.

7. Makhalkina V.V. Processing of semistructured information when building a knowledge base of an expert system of microelement disorders in humans: Abstract of the cand. of boil. sc. Tula: TSU, 2009; 23 p.

8. Bledzhyants G.А., Sarkisyan M.A., Isakova Yu.A., Tumanov N.A., Popov A.N., Begmurodova N.Sh. Key technologies of artificial intelligence formation in medicine. Remedium. 2015;12:10-15.

9. Tarasova I.A. Fuzzy control of medicines introduction process in treatment of hypertensive complications of pregnancy. Eastern European journal of enterprise technologies. 2012; 6/3 (60):12-15.

10. Tarasova I. A. Fuzzy control based on variables with multidimensional membership functions in the diagnosis and treatment of hypertensive complications of pregnancy. Radioelectronic and computer systems. 2012;4:169-173.

11. Shushura A.N., Tarasova I.A. Method of unclear control on the basis of variables with the multidimensional functions of belonging. Artificial Intelligence. 2010;1:122-128.

12. Tarasova I. A. Development of approaches to task the multidimensional membership functions of linguistic variables terms in problems of fuzzy control. Branch Aspects of Technical Sciences. 2014;2(38):11–22.

13. Tarasova I. A. Design principles and architecture of knowledge base of the fuzzy control system based on multidimensional membership functions. Transactions of Kremenchuk Mykhailo Ostrohradskyi National University. 2013;2 (79):56-61.

14. Shushura A.N., Tarasova I.A. Method of specifying the multidimensional membership functions of linguistic variables terms. Information technology and computer engineering. 2013;1(26):39-44.

15. Tarasova I. A. Development of the algorithm of specifying the multidimensional membership functions of linguistic variables terms based on statistical data. Problems of Artificial Intelligence. 2018;2(9):60-70.

16. Tarasova I. A. Setting the membership functions of linguistic variables thermes in the task of determination the dosing of medications in the treatment of the preeclampsia of pregnant women. Izvestiya SFedU. Engineering Sciences. 2019;3:110-121.