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Encoding Clinical Recommendations into Fuzzy DSSs: An Application to COPD Guidelines

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Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 364))

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Abstract

Clinical Decision Support Systems (DSSs) have been applied to medical scenarios by computerizing a set of clinical guidelines of interest, with the final aim of simulating the process followed by the physicians. In this context, fuzzy logic has been profitably used for modeling clinical guidelines affected by uncertainty and improving the interpretability of clinical DSSs through its expressivity close to natural language. However, the task of computerizing clinical guidelines in terms of fuzzy if-then rules can be complex and, often, requires technical capabilities not owned by physicians. In order to face this issue, this paper introduces a fuzzy knowledge editing framework expressly devised and designed to simplify the procedures necessary to codify clinical guidelines in terms of fuzzy if-then rules and linguistic variables. This framework is described with respect to a specific real case regarding the formalization of clinical recommendations extracted from the GOLD guidelines, which contain the best evidence for diagnosing and managing the Chronic Obstructive Pulmonary Disease.

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References

  1. Acampora, G., Loia, V.: Fuzzy markup language: a new solution for transparent intelligent agents. In: IEEE Symposium on Intelligent Agent, pp. 1–6 (2011)

    Google Scholar 

  2. Adeli, A., Neshat, M.: A fuzzy expert system for heart disease diagnosis. In: International Multiconference of Engineering and Computer Scientists, pp. 134–139 (2010)

    Google Scholar 

  3. Alonso, J.M., Magdalena, L., Guillaume, S.: KBCT: a knowledge extraction and representation tool for fuzzy logic based systems. In: IEEE International Conference on Fuzzy Systems, vol. 2, pp. 989–994. Budapest, Hungary (2004)

    Google Scholar 

  4. De Maio, C., Fenza, G., Gaeta, M., Loia, V., Orciuoli, F.: A knowledge-based framework for emergency DSS. Knowl.-Based Syst. 24(8), 1372–1379 (2011)

    Article  Google Scholar 

  5. Guillaume, S., Charnomordic, B.: Learning interpretable fuzzy inference systems with FisPro. Inf. Sci. 181(20), 4409–4427 (2011)

    Article  Google Scholar 

  6. Lahsasna, A., Ainon, R.N., Zainuddin, R., Bulgiba, A.: Design of a fuzzy-based decision support system for coronary heart disease diagnosis. J. Med. Syst. 36(5), 3293–3306 (2012)

    Article  Google Scholar 

  7. Liu, J., Wyatt, J.C., Altman, D.G.: Decision tools in health care: focus on the problem, not the solution. BMC Med. Inf. Decis. Mak. 6, 1–7 (2006)

    Article  Google Scholar 

  8. Minutolo, A., Esposito, M., De Pietro, G.: A fuzzy knowledge-editing framework for encoding guidelines into clinical DSSs. In: 8th International Conference on Knowledge, Information and Creativity Support Systems (KICSS), pp. 379–390 (2013)

    Google Scholar 

  9. 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.) Wireless Mobile Communication and Healthcare. LNICST, vol. 61, pp. 263–270. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  10. Moreno-Velo, F.J., Baturone, I., Sanchez-Solano, S., Barriga, A.: XFuzzy 3.0 A development environment for fuzzy systems. In: 2nd IEEE International Conference on Fuzzy Logic and Technology (EUSFLAT), pp. 93–96 (2001)

    Google Scholar 

  11. Peña-Reyes, C.A., Sipper, M.: A fuzzy-genetic approach to breast cancer diagnosis. Artif. Intell. Med. 17(2), 131–155 (1999)

    Article  Google Scholar 

  12. Sari, A.K., Rahayu, W., Bhatt, M.: Archetype sub-ontology: Improving constraint-based clinical knowledge model in electronic health records. Knowl. Base Syst. 26, 75–85 (2012)

    Article  Google Scholar 

  13. Scott, I.: What are the most effective strategies for improving quality and safety of health care? Intern. Med. J. 39(6), 389–400 (2009)

    Article  Google Scholar 

  14. Shiffman, R.N.: Representation of clinical practice guidelines in conventional and augmented decision tables. J. Am. Med. Inf. Assoc. 4(5), 382–393 (1997)

    Article  Google Scholar 

  15. Thomas, O., Dollmann, T.: Fuzzy-EPC markup language: XML based interchange formats for fuzzy process models. Soft Comput. XML Data Manage. 255, 227–257 (2010)

    Article  Google Scholar 

  16. Tseng, C., Khamisy, W., Vu, T.: Universal fuzzy system representation with XML. Comput. Stand. Interfaces 28(2), 218–230 (2005)

    Article  Google Scholar 

  17. Warren, J., Beliakov, G., Zwaag, B.: Fuzzy logic in clinical practice decision support system. In: 33rd Annual Hawaii International Conference on System Sciences, pp. 4–7 (2000)

    Google Scholar 

  18. Zadeh, L.A.: A theory of approximate reasoning. In: Hayes, J., Michie, D., Mikulich, L.I. (eds.) Machine Intelligence, vol. 9, pp. 149–194. Elsevier, New York (1979)

    Google Scholar 

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Authors and Affiliations

  1. Institute for High Performance Computing and Networking, ICAR-CNR, via P. Castellino, 111-80131, Naples, Italy

    Aniello Minutolo, Massimo Esposito & Giuseppe De Pietro

Authors
  1. Aniello Minutolo
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  2. Massimo Esposito
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  3. Giuseppe De Pietro
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Corresponding author

Correspondence to Aniello Minutolo .

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Editors and Affiliations

  1. Department of Automatic Control and Biomedical Engineering, AGH University of Science and Technology, Kraków, Poland, and International Centre for Decision Sciences and Forecasting, Kraków, Poland

    Andrzej M.J. Skulimowski

  2. Polish Academy of Sciences, Systems Research Institute, Warszawa, Poland, and AGH University of Science and Technology, Kraków, Poland

    Janusz Kacprzyk

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Minutolo, A., Esposito, M., De Pietro, G. (2016). Encoding Clinical Recommendations into Fuzzy DSSs: An Application to COPD Guidelines. In: Skulimowski, A., Kacprzyk, J. (eds) Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions. Advances in Intelligent Systems and Computing, vol 364. Springer, Cham. https://doi.org/10.1007/978-3-319-19090-7_26

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  • DOI: https://doi.org/10.1007/978-3-319-19090-7_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19089-1

  • Online ISBN: 978-3-319-19090-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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