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Fuzzy Cognitive Maps Structure for Medical Decision Support Systems

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Forging New Frontiers: Fuzzy Pioneers II

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 218))

Abstract

Fuzzy Cognitive Maps (FCMs) are a soft computing technique that follows an approach similar to human reasoning and human decision-making process, considering them a valuable modeling and simulation methodology. FCMs can successfully represent knowledge and experience, introducing concepts for the essential elements and through the use of cause and effect relationships among the concepts Medical Decision Systems are complex systems consisting of irrelevant and relevant subsystems and elements, taking into consideration many factors that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall diagnosis with a different degree. Thus, FCMs are suitable to model Medical Decision Support Systems and the appropriate FCM structures are developed as well as corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described.

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References

  • Chaib-draa B, Desharnais J (1998) A Relational Model of Cognitive Maps. International Journal of Human-Computer Studies 49:181–200

    Article  Google Scholar 

  • Craiger JP, Goodman DF, Weiss RJ, Butler A. (1996) Modeling organizational behavior with fuzzy cognitive maps. Journal of Computational Intelligence and Organizations 1:120–123

    Google Scholar 

  • Darley FL, Aronson AE, Brown JR (1969) Differential diagnostic patterns of dysarthria. Journal of Speech and Hearing Research 12:246–269

    Google Scholar 

  • Darley FL, Aronson AE, Brown JR (1969) Clusters of deviant speech dimensions in the dysarthrias. Journal of Speech and Hearing Research 12:462–496

    Google Scholar 

  • Dickerson J, Kosko B (1994) Fuzzy Virtual Worlds. AI Expert 25–31

    Google Scholar 

  • Duffy JR (1995) Motor speech disorders: substrates, differential diagnosis, and management Mosby-Year Book, St. Louis

    Google Scholar 

  • Georgopoulos VC, Malandraki GA, Stylios CD (2003) A Fuzzy Cognitive Map Approach To Differential Diagnosis of Specific Language Impairment. Journal of Artificial Intelligence in Medicine 29:261–278

    Article  Google Scholar 

  • Georgopoulos VC, Stylios CD (2005) Augmented Fuzzy Cognitive Maps Supplemented with Case Base Reasoning for Advanced Medical Decision Support. In: Nikravesh M, Zadeh LA, Kacprzyk J (eds.) Soft Computing for Information Processing and Analysis Enhancing the Power of the Information Technology. Studies in Fuzziness and Soft Computing, Springer-Verlag, pp. 389–398.

    Google Scholar 

  • Georgopoulos VC, Malandraki GA (2005) A Fuzzy Cognitive Map Hierarchical Model for Differential Diagnosis of Dysarthrias and Apraxia of Speech. Proceedings of the 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1–4 September 2005, Shanghai, China.

    Google Scholar 

  • Georgoulas G, Stylios C, Groumpos PP (2006a) Feature Extraction and Classification of Fetal Heart Rate Signals using Wavelet Analysis and Support Vector Machines. International Journal of Artificial Intelligent Tools, 15: 411–432

    Article  Google Scholar 

  • Georgoulas G, Stylios C, Groumpos PP (2006b) Novel Methodology for Fetal Heart Rate Signal Classification During the Intrapartum Period. IEEE Transactions on Biomedical Engineering 53: 875–884.

    Article  Google Scholar 

  • Jain L (1997) Soft Computing Techniques in Knowledge-Based Intelligent Engineering Systems: Approaches and Applications. Studies in Fuzziness and Soft Computing, 10, Springer Verlag

    Google Scholar 

  • Jang J, Sun C, Mizutani E. (1997) Neuro-Fuzzy and Soft Computing. Prentice-Hall, NJ

    Google Scholar 

  • Kakolyris A, Stylios G., Georgopoulos V (2005) Fuzzy Cognitive Maps application for Webmining. Proceedings of the 4th Conference of the European Society for Fuzzy Logic and Technology EUSFLAT-LFA 2005, 7–9 September 2005 Barcelona, Spain

    Google Scholar 

  • Kang I, Lee S (2004) Using fuzzy cognitive map for the relationship management in airline service. Expert Syst. Appl. 26:545–555

    Article  Google Scholar 

  • Khan MS, Khor S,Chong A (2004). Fuzzy cognitive maps with genetic algorithm for goal-oriented decision support. Int. J. Uncertainty, Fuzziness and Knowledge-based Systems. 12:31–42.

    Article  Google Scholar 

  • Kosko B (1992) Neural Networks and Fuzzy Systems. Prentice-Hall, Englewood Cliffs, NJ

    MATH  Google Scholar 

  • Kosko B (1997). Fuzzy Engineering. Prentice-Hall, Englewood Cliffs, NJ

    MATH  Google Scholar 

  • Lee KC, Kim HS (1998) A Causal Knowledge-Driven Inference Engine for Expert System. In Proceedings of the annual Hawaii international conference on system science 284–293.

    Google Scholar 

  • Lee KC, Kin JS, Chung NH, Kwon SJ (2002) Fuzzy cognitive map approach to web-mining inference amplification. Journal of Experts Systems with Applications 22:197–211.

    Article  Google Scholar 

  • Lin CT, Lee CSG (1996) Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Prentice Hall, Upper Saddle River N.J.

    Google Scholar 

  • Malandraki GA, Georgopoulos VC (2006) An Artificial Intelligence System for Differential Diagnosis of Dyslexia and Specific Language Impairment. Chapter to appear In: Dyslexia in Children: New Research, Nova Science Publishers, Inc., Hauppauge, NY

    Google Scholar 

  • McGregor KK, Appel A (2002) On the relation between mental representation and naming in a child with specific language impairment. Clinical Linguistics and Phonetics, 16:1–20

    Article  Google Scholar 

  • Mesarovic MD, Macko D, Takahara Y (1970) The theory of Hierarchical Multilevel Systems. Academic Press, New York.

    Google Scholar 

  • Mitchell CM, Sundstrom GA, (1997) Human interaction with complex systems: design issues and research approaches. IEEE Transactions on Systems, Man, and Cybernetics. 27:265–273

    Google Scholar 

  • Muata K, Bryson O (2004) Generating consistent subjective estimates of the magnitudes of causal relationships in fuzzy cognitive maps. Computers and Operations Research 31: 1165–1175

    Article  MATH  Google Scholar 

  • Papageorgiou EI, Stylios CD, Groumpos PP (2003) An Integrated Two-Level Hierarchical Decision Making System based on Fuzzy Cognitive Maps (FCMs). IEEE Transactions on Biomedical Engineering 50: 1326–1339

    Article  Google Scholar 

  • Papageorgiou EI, Stylios CD, Groumpos PP (2004) Active Hebbian Learning Algorithm to train Fuzzy Cognitive Maps, in International Journal of Approximate Reasoning, Vol. 37, Issue 3, pp. 219–247.

    Article  MATH  MathSciNet  Google Scholar 

  • Papageorgiou EI, Spyridonos P, Stylios CD, Ravazoula R, Groumpos PP, Nikiforidis G (2006) A Soft Computing Method for Tumour Grading Cognitive Maps. Artificial Intelligence in Medicine 36:58–70.

    Article  Google Scholar 

  • Park KS, Kim SH (1995) Fuzzy cognitive maps considering time relationships. International Journal of Human-Computer Studies 42:157–168.

    Article  Google Scholar 

  • Pelaez CE, Bowles JB (1996) Using Fuzzy Cognitive Maps as a System Model for Failure Modes and Effects Analysis. Information Sciences 88:177–199.

    Article  Google Scholar 

  • Pierson JM (1999) Transforming engagement in literacy instruction: The role of student genuine interest and ability. Annals of Dyslexia 49:307–29.

    Article  Google Scholar 

  • Psychcorp, Harcourt Assessment, Inc. (2005) Case Study, No.4.

    Google Scholar 

  • Siljak DD (1979) Overlapping Decentralized Control. In Singh M, Titli A (eds) Large Scale Systems Engineering Applications, North-Holland, New York

    Google Scholar 

  • Stach W, Kurgan L (2004) Modeling Software Development Projects Using Fuzzy Cognitive Maps. Proceedings of the 4th ASERC Workshop on Quantitative and Soft Software Engineering (QSSE’04), Banff, AB, 55–60

    Google Scholar 

  • Stach, W., Kurgan, L., Pedrycz, W., and Reformat M., (2004) “Parallel Fuzzy Cognitive Maps as a Tool for Modeling Software Development Projects” NAFIPS 2004

    Google Scholar 

  • Stylios CD, Groumpos PP, Georgopoulos VC (1999) An Fuzzy Cognitive Maps Approach to Process Control Systems. Journal of Advanced Computational Intelligence 3:409–417.

    Google Scholar 

  • Stylios C, Groumpos P (2000) Fuzzy Cognitive Maps in Modelling Supervisory Control Systems. J Intell Fuzz Syst 8:83–98.

    Google Scholar 

  • Stylios CD (2002) The Knowledge Based Technique of Fuzzy Cognitive Maps for Modeling Complex Systems. Proc. of 16th European Meetings on Cybernetics and Systems Research (EMCSR) April 2 - 5, 2002, University of Vienna, Austria, 524–529.

    Google Scholar 

  • Stylios CD, Groumpos PP (2004) Modeling Complex Systems Using Fuzzy Cognitive Maps. IEEE Transactions on Systems, Man and Cybernetics: Part A Systems and Humans 34: 155–162.

    Article  Google Scholar 

  • Taber R (1991) Knowledge processing with fuzzy cognitive maps. Expert Systems with applications 2:83–87.

    Article  Google Scholar 

  • Van der Lely HKJ (1997) Language and cognitive development in a grammatical SLI boy: Modularity innateness. Journal of Neurolinguistics10:75–107.

    Article  Google Scholar 

  • Xirogiannis G, Stefanou J, Glykas M (2004) A fuzzy cognitive map approach to support urban design. Expert Systems with Applications 26:257–268

    Article  Google Scholar 

  • Zeleznikow J, Nolan J (2001) Using soft computing to build real world intelligent decision support systems in uncertain domains. Decision Support Systems 31:263–285

    Article  Google Scholar 

  • Zhang WR, Chen SS, Bezdek JC (1989) Pool 2: a generic system for cognitive map development and decision analysis. IEEE Transactions on Systems, Man and Cybernetics 19:31–39

    Article  Google Scholar 

  • Zhang WR, Chen SS, Wang W, King RS (1992) A cognitive-map-based approach to the coordination of distributed cooperative agents. IEEE Transactions on Systems, Man and Cybernetics 22:103–114.

    Article  Google Scholar 

Download references

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Stylios, C.D., Georgopoulos, V.C. (2008). Fuzzy Cognitive Maps Structure for Medical Decision Support Systems. In: Forging New Frontiers: Fuzzy Pioneers II. Studies in Fuzziness and Soft Computing, vol 218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73185-6_7

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  • DOI: https://doi.org/10.1007/978-3-540-73185-6_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73184-9

  • Online ISBN: 978-3-540-73185-6

  • eBook Packages: EngineeringEngineering (R0)

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