On the Problem of Medical Diagnostic Evidence: Intelligent Analysis of Empirical Data on Patients in Samples of Limited Size

Esenin-Vol’pin, A.S., About the anti-traditional (ultra-intuitionistic) program of foundations of mathematics and science thinking, Semiotika Inf., 1993, vol. 33, pp. 13–67. https://istina.msu.ru/journals/504250/.

Mnogoznachnye logiki i ikh primeneniya (Multiple-Valued Logics and Their Applications), vol. 2: Logiki v sistemakh iskusstvennogo intellekta (Logics in Artificial Intelligence Systems), Finn, V.K., Ed., Moscow: URSS Izd. LKI, 2008.

Finn, V.K., J.S. Mill’s inductive methods in artificial intelligence systems. Part I, Sci. Tech. Inf. Process., 2011, vol. 38, pp. 385–402; J.S. Mill’s inductive methods in artificial intelligence systems. Part I, Sci. Tech. Inf. Process., 2012, vol. 39, pp. 241–260.

Zabezhailo, M.I., On some estimates of the complexity of calculations in JSM reasoning, Iskusstv. Intell. Prinyatie Reshenii, 2015, no. 1, pp. 3–17.

Zabezhailo, M.I., Some capabilities of enumeration control in the DSM method, Sci. Tech. Inf. Process., 2014, vol. 41, pp. 335–361.

Cohn, P.M., Universal Algebra, Harper & Row, 1965.

Simon, J., On the difference between one and many, Lect. Notes Comput. Sci., 1977, vol. 52, pp. 480–491.

Valiant, L.G., The complexity of enumeration and reliability problems, SIAM J. Comput., 1979, vol. 8, pp. 410–421.

Valiant, L.G., The complexity of computing the permanent, Theor. Comput. Sci., 1979, vol. 8, pp. 189–201.

Nudnov, N.V., Zheludkova, O.G., Mnatsakanova, I.V., Sidorova, E.V., Podoksenova, T.V., and Shevtsov, A.I., Pseudoprogression in a patient with anaplastic ependymoma after radiation therapy, Med. Vizualizatsiya, 2018, no. 2, pp. 18–24.

Hygino da Cruz, L.C., Rodriguez, I., Domingues, R.C., Gasparetto, E.L., and Sorrensen, A.G., Pseudoprogression and pseudoresponse: Imaging challenges in the assessment of post-treatment glioma, Am. J. Neuroradiol., 2011, vol. 32, no. 11, pp. 1978–1985.

Parvez, K., Parvez, A., and Zadeh, G., The diagnosis and treatment of pseudoprogression, radiation necrosis and brain tumor recurrence, Int. J. Mol. Sci., 2014, vol. 15, no. 7, pp. 11832–11846.

Trunin, Yu.Yu., Golanov, A.V., Kostyuchenko, V.V., Galkin, M.V., Khukhlaeva, E.A., and Konovalov, A.N., Pseudoprogression of benign glioma on the example of piloid astrocytoma of the midbrain. Clinical observation, Onkol. Zh.: Luchevaya Diagn. Luchevaya Ter., 2018, vol. 1, no. 1, pp. 94–97.

Trunin, Yu.Yu., Golanov, A.V., Kostyuchenko, V.V., Galkin, M.V., Khukhlaeva, E.A., and Konovalov, A.N., Increased volume of piloid astrocytoma of the midbrain: Relapse or pseudoprogression? Clinical observation, Opukholi Golovy Shei, 2016, vol. 6, no. 1, pp. 68–75.

Trunin, Y., Golanov, A.V., Kostjuchenko, V.V., Galkin, M.V., and Konovalov, A.N., Pilocytic astrocytoma enlargement following irradiation: Relapse or pseudoprogression?, Cureus, 2017. https://www.cureus. com/articles/3962-pilocytic-astrocytoma-enlargement-following-irradiation-relapse-or-pseudoprogression.

Zabezhailo, M.I. and Trunin, Yu.Yu., On medical diagnosis proving: Intelligent analysis of empirical data of patients in limited samples, Slaidy k dokladu na Kongresse “Informatsionnye tekhnologii v meditsine—ITM-2019” (Slides to the Report at the Congress “Information Technologies in Medicine—ITM-2019”), Moscow, 2019. https://itmcongress.ru/itm2019/agenda/ O_dokazatelnosti_%20med-itsinskogo_diagnoza_intellektualnyy_ analiz_empiricheskikh_dannykh_o_patsientakh_/.

Vinogradov, D.V., A probabilistic-combinatorial formal machine learning method based on the lattice theory, in Doctoral (Phys.-Math.) Dissertation, Moscow: Federal Research Center Computer Science and Control, Russian Academy of Sciences, 2018. http://www.frccsc.ru/ diss-council/00207305/diss/list/vinogradov_dv.

Popper, K.R., The Logic of Scientific Discovery, London–New York: Routledge Classics, 1959.

Popper, K.R., Conjectures and Refutations. The Growth of Scientific Knowledge, New York: Basic Books, 1962.