Intelligent decision making system of department of medical institutions based on neural network, production and statistical models

The  article  describes  the  development   of  information-analytical system of decision-making in the treatment  of abdominal  organs of patients. The  structure of the system provides for the formation  of a preliminary  diagnosis of the  patient’s  condition  based  on a neural network  and statistical analysis of the electronic medical record. For the operating control of the patient’s condition  during the operation or getting a quick consultation  in the case of a critical situation,  the system provides an expert assessment  of the circumstances  with  the possibility of a surgeon’s speech dialogue with the intellectual system. Purpose.  Increase the intelligence of decision-making in the department of a medical  in-stitution  based on neural network, production and statistical models.

Materials and methods. Neural networks and the statistical approach for analyzing  and  processing a  large amount  of medical  data,  as well as computer modeling of the practical problem,  using the Java programming language,  were used to obtain scientific results.

Results. The developed prognosis program is a hybrid dynamic expert system,  the use of which  will improve the efficiency  of processes for assessing the severity of the underlying dis-ease,  taking into account pathology; predicting the risk of intraoperative  complications  in the planning mode and in real time; recommendations  of surgical tactics with  combined  surgery; predicting the risk of postoperative  complications; determine  the volume  of intensive  care in the postoperative period.

Conclusion.  The  structure  of creating  a  fuzzy  model  of predicting operational risk for performing simultaneous  interventions  depending on the patient’s  condition  based  on production  rules is considered, the  base  of  which  can  be  corrected  in  the  training  regime  of  the expert system.

1. IT in healthcare [Internet]. URL: https://www.osp.ru/medit/2018/10/13054507.html (Cited: 05.03.19). (In Russ.)

2. Integrated operational rooms [Internet]. URL: http://www.winnermedical.com.ua/integrirovannye-operacionnye-zaly (data obrashcheniya: 10.03.19). (In Russ.)

3. Data analysis technologies [Internet]. URL: https://basegroup.ru/ (Cited: 01.03.19). (In Russ.)

4. C4.5: Algorithms for machine learning. Morgan Kaufmann Publishers [Internet]. URL: https://www.rulequest.com/Personal/ (Cited: 05.03.19). (In Russ.)

5. Borisov A.N., Krumberg O.A., Fedorov I.P. Prinyatiye resheniy na osnove nechetkikh modeley. Primery ispol’zovaniya. = Decision making based on fuzzy models. Examples of using. Riga: Zinatne, 1990. 184 p. (In Russ.)

6. Vertkina N.V., Khamitov F.F. Clinical and economic aspects of simultaneous operations in patients of elderly and senile age Klin. gerontologiya = Clinic gerontology. 2008; 4: 5-10. (In Russ.)

7. Dreyper N., Smit G. Prikladnoy regressionnyy analiz. Mnozhestvennaya regressiya. = Applied regression analysis. Multiple regression. 3rd ed. Moscow: Dialectics; 2007: 912. (In Russ.)

8. Korenevskiy N.A. et al. Proyektirovaniye sistem podderzhki prinyatiya resheniy dlya mediko-ekologicheskikh prilozheniy. = Designing decision support systems for medical and environmental applications. Kursk: KGTU; 2004. 180 p. (In Russ.)

9. Korenevskiy N.A. Designing decision-making systems on fuzzy network models in the tasks of medical diagnostics and forecasting. Vestnik novykh meditsinskikh tekhnologiy = Bulletin of new medical technologies. 2006; XIII; 2: 6-9. (In Russ.)

10. Leonenkov A.V. Nechetkoye modelirovaniye v srede MATLAB i fuzzyTECH = Fuzzy simulation in MATLAB and fuzzyTECH. Saint Petersburg: BHV-Petersburg; 2005. 736 p. (In Russ.)

11. Ruanet V.V., Khetagurova A.K. Informatsionnyye tekhnologii v meditsine – vvedeniye v meditsinskuyu informatiku. = Information technology in medicine - an introduction to medical informatics. Moscow: MAKSPress; 2003. 67 p. (In Russ.)

12. Rykov A.S. Modeli i metody sistemnogo analiza: prinyatiye resheniy i optimizatsiya. Uchebnoye posobiye dlya vuzov. = Models and methods of system analysis: decision making and optimization. Textbook for universities. Moscow: “MISIS”, Publishing house “Ore and metals”; 2005. 352 p. (In Russ.)

13. Selyakova S.M. Fuzzy model and algorithm for solving the problem of the choice of drug therapy. Iskusstvennyy intellekt = Artificial Intelligence. 2014; 1(63): 126-131. (In Russ.)