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A theoretical comparison of disco and CADIAG-II-like systems for medical diagnoses
Kybernetika, Tome 42 (2006) no. 6, pp. 723-748 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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In this paper a fuzzy relation-based framework is shown to be suitable to describe not only knowledge-based medical systems, explicitly using fuzzy approaches, but other ways of knowledge representation and processing. A particular example, the practically tested medical expert system Disco, is investigated from this point of view. The system is described in the fuzzy relation-based framework and compared with CADIAG-II-like systems that are a “pattern” for computer-assisted diagnosis systems based on a fuzzy technology. Similarities and discrepancies in – representation of knowledge, patient’s information, inference mechanism and interpretation of results (diagnoses) – of the systems are established. This work can be considered as another step towards a general framework for computer-assisted medical diagnosis.
In this paper a fuzzy relation-based framework is shown to be suitable to describe not only knowledge-based medical systems, explicitly using fuzzy approaches, but other ways of knowledge representation and processing. A particular example, the practically tested medical expert system Disco, is investigated from this point of view. The system is described in the fuzzy relation-based framework and compared with CADIAG-II-like systems that are a “pattern” for computer-assisted diagnosis systems based on a fuzzy technology. Similarities and discrepancies in – representation of knowledge, patient’s information, inference mechanism and interpretation of results (diagnoses) – of the systems are established. This work can be considered as another step towards a general framework for computer-assisted medical diagnosis.
Classification : 03B52, 03E72, 62F15, 92C50
Keywords: fuzzy relations; medical diagnoses 
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Kiseliova, Tatiana. A theoretical comparison of disco and CADIAG-II-like systems for medical diagnoses. Kybernetika, Tome 42 (2006) no. 6, pp. 723-748. http://geodesic.mathdoc.fr/item/KYB_2006_42_6_a6/

[1] Adlassnig K.-P., Kolarz G.: CADIAG-II: computer-assisted medical diagnosis using fuzzy subsets. In: Approximate Reasoning in Decision Analysis (M. M. Gupta and E. Sanchez, eds.), North Holland, Amsterdam 1982, pp. 495–505

[2] Baldwin J. F., Pilsworth B. W., Norris D.: Medical diagnoses from patient records. Fuzzy Sets and Systems 23 (1987), 73–87 | MR

[3] Boegl K.: Design and Implementation of a Web-Based Knowledge Acquisition Toolkit for Medical Expert Consultation Systems. Ph.D. Thesis, Technische Universität Wien, 1997

[4] Boegl K., Adlassnig K.-P., Hayashi Y., Rothenfluh, T., Leitich H.: Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system. Artificial Intelligence in Medicine 30 (2004), 1–26 | DOI

[5] Daniel M.: Theoretical comparison of inference in CADIAG and MYCIN-like systems. Tatra Mountains Math. Publ. 16 (1999), 255–272 | MR

[6] Daniel M., Hájek, P., Nguyen H.: CADIAG-2 and MYCIN-like systems. Artificial Intelligence in Medicine 9 (1997), 241–259 | DOI

[7] Dubois D., Prade H.: Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York 1980 | MR | Zbl

[8] Dubois D., Prade H.: A review of fuzzy aggregation connectives. Inform. Sci. 36 (1985), 1–2, 85–121 | DOI | MR

[9] Fullèr R.: Introduction to Neuro-fuzzy Systems. Physica-Verlag, Heidelberg 2000 | MR | Zbl

[10] Hájek P., Havránek, T., Jiroušek R.: Uncertain Information Processing in Expert Systems. CRC Press, Boca Raton, Florida 1992 | MR

[11] Hájek P., Valdés J.: An analysis of MYCIN-like expert systems. Mathware and Soft Computing 1 (1994), 45–68 | MR

[12] Hayes-Roth F.: Expert systems. In: Encyclopedia of Artificial Intelligence – Second edition (S. C. Shapiro, ed.), Volume 1, Wiley, New York 1992, pp. 477–489

[13] Kiseliova T.: A Computer Assisted Method in Medical Diagnosis Using New Information Technologies. Ph.D. Thesis, Tbilisi State University, Georgia Technical University 1995

[14] Klement E.-P., Mesiar, R., Pap E.: Triangular Norms. Kluwer, Dordrecht 2000 | MR | Zbl

[15] Klír G. J., Clair U. H., Yuan B.: Fuzzy Sets Theory, Foundations and Applications. Prentice Hall, Upper Saddle River, NJ 1997 | MR

[16] Klír G. J., Wierman M. J.: Uncertainty–Based Information. Physica–Verlag, Heidelberg 1998 | MR | Zbl

[17] Klír G. J., Yuan B.: Fuzzy Sets and Fuzzy Logic. Theory and Applications. Prentice Hall PTR, Upper Saddle River, NJ 1995 | MR | Zbl

[18] Kolousek G.: The System Architecture of an Integrated Medical Consultation System and Its Implementation Based on Fuzzy Technology. Ph.D. Thesis, Technische Universität Wien 1997

[19] Leitich H., Adlassnig K.-P., Kolarz G.: Evaluation of two different models of semi-automatic knowledge acquisition for the medical consultant system CADIAG-II/RHEUMA. Artificial Intelligence in Medicine 25 (2002), 215–225 | DOI

[20] Melle W. J. V., Shortliffe E. H., Buchanan B. G.: EMYCIN: A knowledge engineer’s tool for constructing rule-based expert systems. In: Rule-Based Expert Systems. Addison-Wesley, Reading, MA 1984

[21] Mendel J. M.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice Hall PTR, Upper Saddle River 2000 | Zbl

[22] Menger K.: Statistical metric spaces. Proc. Nat. Acad. Sci. U. S. A. 28 (1942), 535–537 | DOI | MR

[23] Nguyen P. H.: Some Contributions to Fuzzy Expert Systems with Emphasis on Medicine. Ph.D. Thesis, Technische Universität Wien 1995

[24] Novák V.: Fuzzy logic deduction with words applied to ancient sea level estimation. In: Fuzzy Logic in Geology (R. Demicco and G. J. Klír, eds.), Academic Press, Amsterdam 2003, pp. 301–336

[25] Novák V.: Fuzzy relation equations with words. In: Fuzzy Partial Differential Equations and Relational Equations: Reservoir Characterization and Modeling, Studies in Fuzziness and Soft Computing (M. Nikravesh, L. A. Zadeh, and V. Korotkikh, eds.) Physica–Verlag, Heidelberg 2004, pp. 167–185 | MR | Zbl

[26] Novák V., Perfilieva I.: On the semantics of perception-based fuzzy logic deduction. Internat. J. Intelligent Systems 19 (2004), 1007–1031 | DOI | Zbl

[27] Novák V., Perfilieva, I., Močkoř J.: Mathematical Principles of Fuzzy Logic. Kluwer, Boston – Dordrecht 1999 | Zbl

[28] Rothenfluh T. E., Bögl, K., Adlassnig K.-P.: Representation and acquisition of knowledge for a fuzzy medical consultation system. In: Fuzzy Systems in Medicine (P. S. Szczepaniak, P. J. G. Lisboa, and J. Kacprzyk, eds.), Physica Verlag, Heidelberg – New York 2000, pp. 636–651

[29] Sanchez E.: Medical diagnosis and composite fuzzy relations. In: Advances in Fuzzy Set Theory and Applications (M. M. Gupta, R. K. Ragade, and R. R. Yager, eds.), North–Holland, New York 1979, pp. 437–444 | MR

[30] Schweizer B., Sklar A.: Probabilistic Metric Spaces. North Holland, Amsterdam 1983 | MR | Zbl

[31] Seising R., Schuh C., Adlassnig, and K.-P.: Medical knowledge, fuzzy sets, expert systems. In: Workshop on Intelligent and Adaptive Systems in Medicine, Prague 2003, http://cyber.felk.cvut.cz/EUNITE03-BIO

[32] Shortliffe E. H.: Computer–Based Medical Consultations, MYCIN. Elsevier, North–Holland, New York 1976

[33] Thiele H.//Einführung in die Fuzzy-Logik, University of Dortmund: Spezialvorlesung. Script, ftp:. ftp.informatik.uni-dortmund.de/pub/ls01.info/fuzzy.logik/skript, 1998

[34] Thiele H.: Interpreting linguistic hedges by concepts of functional analysis and mathematical logic. In: 6th European Congress on Intelligent Techniques and Soft Computing, EUFIT’98, Aachen 1008, pp. 114–120

[35] Yager R.: Level sets for membership evaluation of fuzzy subsets. In: Fuzzy Set and Possibility Theory, Recent Developments (R. Yager et al., eds.), Pergamon Press, 1982, pp. 90–97

[36] Yager R. R.: Fuzzy logic method in recommended systems. Fuzzy Sets and Systems 136 (2003), 133–149 | DOI | MR

[37] Yager R. R., Filev D. P.: Essentials of Fuzzy Modelling and Control. Ketherine Schowalter, 1994

[38] Zadeh L. A.: Fuzzy sets. Inform. and Control 8 (1965), 338–353 | DOI | MR | Zbl

[39] Zadeh L. A.: The concept of a linguistic variable and its application to approximate reasoning, Part I, Part II, Part III. Inform. Sci. 8, 9 (1975), 199–249, 301–357, 43–80

[40] Zhou X.-H., Obuchowski N. A., McClish D. K.: Statistical Methods in Diagnostic Medicine. (Wiley Series in Probability and Statistics.) Wiley, New York 2002 | MR

[41] Zimmermann H.-J.: Fuzzy Set Theory and Its Applications. Kluwer Academic Publishers, Dordrecht 1991 | MR | Zbl