Abstract
Clinical practice guidelines are expected to promote more consistent, effective, and efficient medical practices, especially if implemented in clinical Decision Support Systems (DSSs). With the goal of properly representing and efficiently handling clinical guidelines affected by uncertainty and inter-connected between them, this paper proposes a hybrid fuzzy inference approach for building fuzzy DSSs. It provides a set of specifically devised functionalities for best modeling and reasoning on the particular clinical knowledge underpinning guidelines: i) it organizes the whole fuzzy DSS into self-contained sub-systems which are able to independently reason on piece of knowledge according to their peculiar inference scheme; ii) a global inference scheme has been defined for handling and reasoning on such sub-systems, according to the classical crisp expert system approach. As a proof of concept, the proposed approach has been applied to a practical case, showing its capability of supporting multiple levels of inference and, thus, highlighting the possibility of being profitably used to model and reason on complex clinical guidelines in actual medical scenarios.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Leape, L.: Practice guidelines and standards: An overview. QRB, Quality Review Bulletin 16(2), 42 (1990)
Wang, D., Peleg, M., Tu, S.W., Boxwala, A.A., Greenes, R.A., Patel, V.L., Shortliffe, E.H.: Representation primitives, process models and patient data in computer-interpretable clinical practice guidelines: A literature review of guideline representation models. Int. Journal Med. Inform. 68(1-3), 59–70 (2002)
Ainon, R.N., Bulgiba, A.M., Lahsasna, A.: AMI Screening Using Linguistic Fuzzy Rules. Journal of Medical Systems 36(2), 463–473 (2012)
Adeli, A., Neshat, M.: A fuzzy expert system for heart disease diagnosis. In: Proc. of International Multiconference of Engineering and Computer Scientists, pp. 134–139 (2010)
Lahsasna, A., Ainon, R.N., Zainuddin, R., Bulgiba, A.: Design of a Fuzzy-based Decision Support System for Coronary Heart Disease Diagnosis. JM Syst. 36, 3293–3306 (2012)
Shiffman, R.: Representation of clinical practice guidelines in conventional and augmented decision tables. J. of the American Medical Informatics Association 4(5), 382–393 (1997)
Zadeh, L.A.: A theory of approximate reasoning. In: Machine Intelligence, pp. 149–194. John Wiley & Sons, New York (1979)
Torra, V.: A review of the construction of hierarchical fuzzy systems. Int. J. Intell. Syst. 17, 531–543 (2002)
Sottara, D., Mello, P., Proctor, M.: Adding Uncertainty to a Rete-OO Inference Engine. In: Proc. of the Int. Symposium on Rule Representation, Interchange and Reasoning on the Web, pp. 104–118 (October 2008)
Pan, J., Desouza, G.N., Kak, A.C.: Fuzzyshell: a large-scale expert system shell using fuzzy logic for uncertainty reasoning. IEEE Trans. Fuzzy Syst. 6, 563–581 (1998)
Corchado, E., Graña, M., Wozniak, M.: New trends and applications on hybrid artificial intelligence systems. Neurocomputing 75(1), 61–63 (2012)
Corchado, E., Abraham, A., Carvalho, A.: Hybrid intelligent algorithms and applications. Information Sciences 180(14), 2633–2634 (2010)
Esposito, M., De Falco, I., De Pietro, G.: An evolutionary-fuzzy DSS for assessing health status in multiple sclerosis disease. Int. J. of Med. Inf. 80(12), e245–e254 (2011)
Minutolo, A., Esposito, M., De Pietro, G.: A Fuzzy Decision Support Language for building Mobile DSSs for Healthcare Applications. In: Godara, B., Nikita, K.S. (eds.) MobiHealth 2012. LNICST, vol. 61, pp. 263–270. Springer, Heidelberg (2013)
Setnes, M., Babuska, R., Kaymak, U., van Nauta Lemke, H.: Similarity measures in fuzzy rule base simplification. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics 28(3), 376–386 (1998)
Rabe, K., Hurd, S., Anzueto, A., Barnes, P., Buist, S., Calverley, P., Fukuchi, Y., Jenkins, C., Rodriguez-Roisin, R., van Weel, C., et al.: Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: Gold executive summary. American Journal of Respiratory and Critical Care Medicine 176(6), 532 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Minutolo, A., Esposito, M., De Pietro, G. (2013). A Hybrid Inference Approach for Building Fuzzy DSSs Based on Clinical Guidelines. In: Pan, JS., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2013. Lecture Notes in Computer Science(), vol 8073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40846-5_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-40846-5_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40845-8
Online ISBN: 978-3-642-40846-5
eBook Packages: Computer ScienceComputer Science (R0)