ISSN 2071-8594

Russian academy of sciences


Gennady Osipov

B.A. Kobrinskii Certainty factors triunity in the medical diagnostics tasks


The paper suggests approaches to investigating and solving the problem of three factors characterizing the measure of the experts' confidence in the occurrence of symptoms in diseases, the timing of the manifestation of symptoms and the frequency of symptoms in progressive hereditary diseases in five age groups that differ in clinical manifestations (polyvariant character space). Linguistic scales of fuzzy characteristics (interval age and occurrence of signs) and certainty factors should contribute to a more accurate and accurate evaluation of diagnostically significant traits and to increase the effectiveness of diagnosis at different ages. The measure of confidence is determined with respect to each characteristic used for a given nosological form. In the process of assessing risk factors, specific features of experts' thinking are considered – intuition, confidence in their knowledge and reflexivity (regarding emerging hypotheses). Extraction of knowledge is expected from two or more experts. Various stages and variants of group expertise with the participation of a cognitive scientist are considered. Certainty factors are an important condition for increasing the reliability of expert decisions in the diagnosis of orphan hereditary diseases, which does not allow to draw on the extraction of knowledge from the databases of case histories or on a set of literary sources.


knowledge engineering, three certainty factors for a feature, fuzzy knowledge, linguistic scales, expert reflexivity, group knowledge extraction, orphan diseases, lysosomal storage diseases.

PP. 62-72.


1. Novikov P.V. 2012. Problema redkikh (orfannykh) nasledstvennykh boleznej u detej v Rossii i puti ee resheniya [The problem of rare (orphan) hereditary diseases in children in Russia and the ways to solve it]. Rossijskij vestnik perinatologii i pediatrii [Russian bulletin of perinatology and pediatrics] 57 (2):4-8.
2. Gupta S. 2012. [Electronic resource] Rare diseases: Canada’s «research orphans». Open Medicine. - № 6(1). - e23-27. URL: (date of the application: 24.02.2018).
3. Novikov P.V. 2013. Pravovye aspekty redkikh (orfannykh) zabolevanij v Rossii i v mire [Legal aspects of rare (orphan) diseases in Russia and in the world]. Zhurnal «Meditsina» [Journal of Medicine] 4:53-73.
4. Ayme S., Caraboenf M., Gouvernet J. GENDIAG: A computer assisted syndrome identification system // Clinical Genetics. – 1985. – Vol.28, No.5. – P.410-411.
5. Pitt D.B., Bankier A., Haan E.A. A visual verbal computer assisted syndrome identification system // Australian Paediatrics Journal. -1985. – Vol.21, No.4. – P.306-307.
6. Kobrinsky B., Kazantseva L., Feldman A., Veltishchev Ju. Computer diagnosis of hereditary childhood diseases // Medical Audit News. – 1991. –Vol.1, No.4. – P.52-53.
7. Schorderet D.F. Using OMIM (On-line Mendelian Inheritance in Man) as an expert system in medical genetics // American Journal of Medical Genetics. – 1991. –Vol.1, No.39(3). – P.278-284.
8. Guest S.S., Evans C.D., Winter R.M. The online London dysmorphology database // Genetics in Medicine. – 1999. – Vol.1, No.5. –P.207-212.
9. Douzgou S., Clayton-Smith J., Gardner S., Day R., Griffiths P., Strong K. et al. Dysmorphology at a distance: results of a web-based diagnostic service // European Journals of Human Genetics. – 2014. –Vol. 22, No.3. –P.327–332.
10. Vagin V.N., Golovina E.YU., Zagoryanskaya А.А., Fomina M.V. Dostovernyj pravdopodobnyj vyvod v intellektual'nykh sistemakh [Reliable probability deduction in intelligent systems]. 2 ed. – Moskva: Fizmatlit, 2008. 712 p.
11. Shortliffe E.H., Buchanan B.G. A Model of Inexact Reasoning in Medicine // Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project / B.G. Buchanan and E.H. Shortliffe (Eds.). – Reading, London, Amsterdam, Sydney: Addison-Wesley Publishing Company, 1984. –P.233-262.
12. Adams J.B. Probabilistic Reasoning and Certainty Factors // Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project / B.G. Buchanan and E.H. Shortliffe (Eds.). – Reading, London, Amsterdam, Sydney: Addison-Wesley Publishing Company, 1984. – P.263-271.
13. Van Melle W. The Structure of the MYCIN System // Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project / Buchanan, B.G. and Shortliffe, E.H. (eds.). – Reading et al.: Addison-Wesley, 1984. – P.67-77.
14. Luger G.F. Artificial Intelligence: Structures and strategies for complex problem solving. 6th ed. – Boston, San Francisco, New York et al.: Pearson, 2009. – 784 p.
15. Nalepa G.J. Proposal of business process and rules modeling with the XTT method // Symbolic and numeric algorithms for scientific computing / Negru V. et al. (eds.). SYNASC Ninth International Symposium. September 26-29, 2007. – Los Alamos, California, Washington, Tokyo: IEEE Computer Society. IEEE. CPS Conf. Publ. Service, 2007. – P.500-506.
16. Nalepa G.J. Modeling with rules using semantic knowledge engineering. – Springer, 2018. – 430 p.
17. Pospelov D.A, Osipov G.S. 2002. Vvedenie v prikladnuyu semiotiku. Glava 5. Operatsii v semioticheskikh bazakh znanij [Introduction to applied semiotics. Chapter 5. Operations in semiotic knowledge bases]. Iskusstvennyj intellekt [Artificial Intelligence]. 6 (54):28-35.
18. Dlugach T.B. 2010. Sushhnost' i yavlenie [Essence and phenomenon]. Novaya filosofskaya ehntsiklopediya [New philosophical encyclopedia]. Moskva: Mysl'. Vol.III. P.682-683.
19. Kant I. 2016. Kritika chistogo razuman [Criticism of pure reason]. Moskva: Ehksmo. 736 p.
20. Kobrinskii B. Expert reflection in the process of diagnosis of diseases at the extraction of knowledge // Advances in Computer Science Research: Proceedings of the IV International Research Conference "Information Technologies in Science, Management, Social Sphere and Medicine" (ITSMSSM 2017). – 2017. – Vol.72. –P.321-323. [Electronic resource] URL: (date of the application: 01.03.2018)
21. Zade L.A. Fuzzy sets // Information and Control. – 1965. –Vol.8, Iss.3. – P.338-353.
22. Zade L. Ponyatie lingvisticheskoj peremennoj i ego primenenie k prinyatiyu priblizhennykh reshenij [The concept of a linguistic variable and its application to the adoption of approximate solutions]. Moskva: Mir. 165 p.
23. Pospelov D.A. 1994. Serye i/ili cherno-belye? [Gray and / or black and white?]. Prikladnaya ehrgonomika. Spets. vypusk. Refleksivnye protsessy [Applied ergonomics. Specialist. release. Reflexive processes]. 1:29-33.
24. Lotman Yu.M. 2005. Ob iskusstve [On art]. S.-Peterburg: Iskusstvo – SPB. 702 с.
25. Nazarenko G.I., Kleymenova E.B., Payushik S.A., Otdelenov V.A, Sychev D.A, Yashina L.P. Decision support systems in clinical practice: The case of venous thromboembolism prevention // International Journal Risk Safety Medicine. – 2015. – Vol.27, spec. Iss. – P.S104-0S105.
26. Larichev O.I. 2008. Teoriya i metody prinyatiya reshenij, a takzhe KHronika sobytij v Volshebnykh stranakh: Uchebnik. Izd. tret'e, pererab. i dop. [Theory and Methods of Decision Making, and Chronicle of Events in Magical Countries: A Textbook. Ed. third, revised. and additional.]. Moskva: Universitetskaya kniga, Logos. 392 p.
27. Kobrinskii B.A. 1996. K voprosu ucheta obraznogo myshleniya i intuitsii v ehkspertnykh meditsinskikh sistemakh [To the question of accounting for image thinking and intuition in expert medical systems] V Natsional'naya konferentsiya s mezhdunarodnym uchastiem "Iskusstvennyj intellekt-96". Sbornik nauchnykh trudov. T.2. [V National Conference with international participation "Artificial Intelligence-96". Collection of scientific papers. V.2]. Moskva: Fizmatlit. P.207-210.
28. Trakhtengerts Eh.L. 2001. Neopredelennost' v modelyakh komp'yuternykh sistem podderzhki prinyatiya reshenij [Uncertainty in the models of computer decision support systems]. Iskusstvennyj intellekt [Artificial Intelligence]. 5-6:3-11.
29. Kobrinskii B.A. 2005. Retrospektivnyj analiz meditsinskikh ehkspertnykh sistem [A Retrospective Analysis of Medical Expert Systems]. Novosti iskusstvennogo intellekta [News of Artificial Intelligence]. 2:6-17.
30. Zade L.А. 2001. Rol' myagkikh vychislenij i nechetkoj logiki v ponimanii, konstruirovanii i razvitii informatsionnykh/
intellektual'nykh sistem [The role of soft computing and fuzzy logic in understanding, designing and developing
information/intelligent systems]. Iskusstvennyj intellekt [Artificial Intelligence]. 2-3:7-11.
31. kh'ell L., Zigler D. 2003. Teorii lichnosti. 3-e mezhdunarodnoe izdanie [Theories of personality. 3rd international edition]. SPb: Piter. 608 p.
32. Price P.C. Psychology Research Methods Core Skills and Concepts v. 1.0. Book Archive, 2012. [Electronic resource]. URL: (date of the application: 19.01.2018)/
33. Rich E., Knight K, Nair S.B. Artificial Intelligence. Third ed. – New Deli: Nana McGraw-Hill Publ. Co. Ltd, 2009. – 478 p.