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Certainty Factor Triunity in Medical Diagnostics Tasks

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Abstract

This paper suggests approaches to investigating and solving the problem of three factors that characterize the measure of expert 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 (a polyvariant character space). Linguistic scales of fuzzy characteristics (interval age and the occurrence of signs) and certainty factors should contribute to a more subtle and accurate evaluation of diagnostically significant traits and 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 the thinking of experts are considered, that is, intuition, confidence in their knowledge, and reflexivity (regarding emerging hypotheses). Various stages and variants of group expertise with the participation of a cognitive scientist are considered.

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REFERENCES

  1. Novikov, P.V., The problem of rare (orphan) hereditary diseases in children in Russia and the ways to solve it, Ross. Vestn. Perinatol. Pediatr., 2012, vol. 57, no. 2, pp. 4–8.

    Google Scholar 

  2. Gupta, S., Rare diseases: Canada’s “research orphans,” Open Med., 2012, no. 6, pp. e23–27. http://sid.usal.es/idocs/F8/ART20711/gupta.pdf. Accessed February 24, 2018.

  3. Novikov, P.V., Legal aspects of rare (orphan) diseases in Russia and in the world, Meditsina, 2013, no. 4, pp. 53–73.

  4. Ayme, S., Caraboenf, M., and Gouvernet, J., GENDIAG: A computer assisted syndrome identification system, Clin. Genet., 1985, vol. 28, no. 5, pp. 410–411.

    Google Scholar 

  5. Pitt, D.B., Bankier, A., and Haan, E.A., A visual verbal computer assisted syndrome identification system, Austr. Paediatr. J., 1985, vol. 21, no. 4, pp. 306–307.

    Google Scholar 

  6. Kobrinsky, B., Kazantseva, L., Feldman, A., and Veltishchev, Ju., Computer diagnosis of hereditary childhood diseases, Med. Audit News, 1991, vol. 1, no. 4, pp. 52–53.

    Google Scholar 

  7. Schorderet, D.F., Using OMIM (On-Line Mendelian Inheritance in Man) as an expert system in medical genetics, Am. J. Med. Genet., 1991, vol. 1, no. 39, pp. 278–284.

    Article  Google Scholar 

  8. Guest, S.S., Evans, C.D., and Winter, R.M., The online London dysmorphology database, Genet. Med., 1999, vol. 1, no. 5, pp. 207–212.

    Article  Google Scholar 

  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, Eur. J. Hum. Genet., 2014, vol. 22, no. 3, pp. 327–332.

    Article  Google Scholar 

  10. Vagin, V.N., Golovina, E.Yu., Zagoryanskaya, A.A., and Fomina, M.V., Dostovernyi pravdopodobnyi vyvod v intellektual’nykh sistemakh (Reliable Probability Deduction in Intelligent Systems), Moscow: Fizmatlit, 2008, 2nd ed.

  11. Shortliffe, E.H. and Buchanan, B.G., A model of inexact reasoning in medicine, in Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project, Buchanan, B.G. and Shortliffe, E.H., Eds., Addison-Wesley Publishing Company, 1984, pp. 233–262.

    Google Scholar 

  12. Adams, J.B., Probabilistic reasoning and certainty factors, in Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project, Buchanan, B.G. and Shortliffe, E.H., Eds., Reading, Addison-Wesley Publishing Company, 1984, pp. 263–271.

    Google Scholar 

  13. Van Melle, W., The structure of the MYCIN system, in Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project, Buchanan, B.G. and Shortliffe, E.H., Eds., Addison-Wesley Publishing Company, 1984, pp. 67–77.

    Google Scholar 

  14. Luger, G.F., Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Pearson, 2009, 6th ed.

    Google Scholar 

  15. Nalepa, G.J., Proposal of business process and rules modeling with the XTT method, in Symbolic and Numeric Algorithms for Scientific Computing, SYNASC Ninth International Symposium. September 26–29,2007, 2007, pp. 500–506.

  16. Nalepa, G.J., Modeling with Rules Using Semantic Knowledge Engineering, Springer, 2018.

    Book  Google Scholar 

  17. Pospelov, D.A. and Osipov, G.S., Introduction to applied semiotics. Chapter 5. Operations in semiotic knowledge bases, Iskusstv. Intell., 2002, vol. 6, no. 54, pp. 28–35.

  18. Dlugach, T.B., Essence and phenomenon, in Novaya filosofskaya ehntsiklopediya (New Philosophical Encyclopedia), Moscow: Mysl’, 2010, vol. 3, pp. 682–683.

  19. Kant, I., Kritik der reinen Vernunft, 1781.

  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, pp. 321–323. https://www.atlantis-press.com/proceedings/itsmssm-17. Accessed March 1, 2018.

  21. Zadeh, L.A., Fuzzy sets, Inf. Control, 1965, vol. 8, no. 3, pp. 338–353.

    Article  Google Scholar 

  22. Zadeh, L., The concept of a linguistic variable and its application to approximate reasoning—I, Inf. Sci., 1975, vol. 8, no. 3, pp. 199–249; The concept of a linguistic variable and its application to approximate reasoning—II, The concept of a linguistic variable and its application to approximate reasoning—II, Inf. Sci., 1975, vol. 8, no. 4, pp. 301–357; The concept of a linguistic variable and its application to approximate reasoning—III, Inf. Sci., 1975, vol. 9, no. 1, pp. 43–80.

    Article  Google Scholar 

  23. Pospelov, D.A., Gray and/or black and white?, Prikl. Ergon., 1994, no. 1, pp. 29–33.

  24. Lotman, Yu.M., Ob iskusstve (About Art), St. Petersburg: Iskusstvo, 2005.

  25. Nazarenko, G.I., Kleymenova, E.B., Payushik, S.A., Otdelenov, V.A., Sychev, D.A., and Yashina, L.P., Decision support systems in clinical practice: The case of venous thromboembolism prevention, Int. J. Risk Saf. Med., 2015, vol. 27, pp. S104–S105.

    Article  Google Scholar 

  26. Larichev, O.I., Teoriya i metody prinyatiya reshenii, a takzhe Khronika sobytii v volshebnykh stranakh: Uchebnik (Theory and Methods of Decision Making, as well as the Chronicle of Events in Magical Countries: Textbook), Moscow: Univ. Kn., Logos, 2008, 3rd ed.

  27. Kobrinskii, B.A., To the question of taking into account visual thinking and intuition in expert medical systems, V Natsional’naya konferentsiya s mezhdunarodnym uchastiem “Iskusstvennyi intellekt-96.” Sbornik nauchnykh trudov (V National Conference with International Participation Artificial Intelligence-96. Proceedings), 1996, vol. 2, pp. 207–210.

  28. Trakhtengerts, E.L., Uncertainty in models of computer decision support systems, Iskusstv. Intell., 2001, nos. 5–6, pp. 3–11.

  29. Kobrinskii, B.A., A retrospective analysis of medical expert systems, Nov. Iskusstv. Intell., 2005, no. 2, pp. 6–17.

  30. Zadeh, L.A., The role of soft computing and fuzzy logic in understanding, designing and developing information/intelligent systems, Iskusstv. Intell., 2001, nos. 2–3, pp. 7–11.

  31. Hjelle, L.A. and Ziegler, D.J., Personality Theories: Basic Assumptions, Research, and Applications, McGraw-Hill, 1992.

    Google Scholar 

  32. Price, P.C., Psychology Research Methods Core Skills and Concepts v. 1.0. Book Archive, 2012. https://2012books.lardbucket.org/pdfs/psychologyresearch-methods-core-skills-and-concepts.pdf. Accessed January 19, 2018.

  33. Rich, E., Knight, K., and Nair, S.B., Artificial Intelligence, New Deli: Nana McGraw-Hill Publ. Co. Ltd, 2009, 3rd ed.

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Authors and Affiliations

  1. Institute for Modern Information Technologies in Medicine, Federal Research Center Computer Science and Control, Russian Academy of Sciences, 119333, Moscow, Russia

    B. A. Kobrinskii

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  1. B. A. Kobrinskii
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Correspondence to B. A. Kobrinskii.

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Translated by L. Solovyova

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Kobrinskii, B.A. Certainty Factor Triunity in Medical Diagnostics Tasks. Sci. Tech. Inf. Proc. 46, 321–327 (2019). https://doi.org/10.3103/S0147688219050046

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