Advertisement

A Comparative Study of Defeasible Argumentation and Non-monotonic Fuzzy Reasoning for Elderly Survival Prediction Using Biomarkers

  • Lucas RizzoEmail author
  • Ljiljana Majnaric
  • Luca Longo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11298)

Abstract

Computational argumentation has been gaining momentum as a solid theoretical research discipline for inference under uncertainty with incomplete and contradicting knowledge. However, its practical counterpart is underdeveloped, with a lack of studies focused on the investigation of its impact in real-world settings and with real knowledge. In this study, computational argumentation is compared against non-monotonic fuzzy reasoning and evaluated in the domain of biological markers for the prediction of mortality in an elderly population. Different non-monotonic argument-based models and fuzzy reasoning models have been designed using an extensive knowledge base gathered from an expert in the field. An analysis of the true positive and false positive rate of the inferences of such models has been performed. Findings indicate a superior inferential capacity of the designed argument-based models.

Keywords

Argumentation Theory Non-monotonic reasoning Defeasible reasoning Fuzzy reasoning Possibility Theory Biomarkers 

Notes

Acknowledgments

Lucas Middeldorf Rizzo would like to thank CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) for his Science Without Borders scholarship, proc n. 232822/2014-0.

References

  1. 1.
    Barron, E., Lara, J., White, M., Mathers, J.C.: Blood-borne biomarkers of mortality risk: systematic review of cohort studies. PloS One 10(6), e0127550 (2015)CrossRefGoogle Scholar
  2. 2.
    Bench-Capon, T.J., Dunne, P.E.: Argumentation in artificial intelligence. Artif. Intell. 171(10–15), 619–641 (2007)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Besnard, P., Hunter, A.: A logic-based theory of deductive arguments. Artif. Intell. 128(1–2), 203–235 (2001)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Castro, J.L., Trillas, E., Zurita, J.M.: Non-monotonic fuzzy reasoning. Fuzzy Sets Syst. 94(2), 217–225 (1998)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Chesñevar, C.I., Maguitman, A.G., Loui, R.P.: Logical models of argument. ACM Comput. Surv. (CSUR) 32(4), 337–383 (2000)CrossRefGoogle Scholar
  6. 6.
    De Ruijter, W., et al.: Use of framingham risk score and new biomarkers to predict cardiovascular mortality in older people: population based observational cohort study. BMJ 338, a3083 (2009)CrossRefGoogle Scholar
  7. 7.
    Dubois, D., Prade, H.: Possibility theory: qualitative and quantitative aspects. In: Smets, P. (ed.) Quantified Representation of Uncertainty and Imprecision, pp. 169–226. Springer, Dordrecht (1998).  https://doi.org/10.1007/978-94-017-1735-9_6CrossRefzbMATHGoogle Scholar
  8. 8.
    Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and N-person games. Artif. Intell. 77(2), 321–358 (1995)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Gegov, A., Gobalakrishnan, N., Sanders, D.: Rule base compression in fuzzy systems by filtration of non-monotonic rules. J. Intell. Fuzzy Syst. 27(4), 2029–2043 (2014)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Group, B.D.W., et al.: Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin. Pharmacol. Ther. 69(3), 89–95 (2001)CrossRefGoogle Scholar
  11. 11.
    Lee, S., Lindquist, K., Segal, M., Covinsky, K.: Development and validation of a prognostic index for 4-year mortality in older adults. Jama 295(7), 801–808 (2006)CrossRefGoogle Scholar
  12. 12.
    Lloyd-Jones, D., Adams, R., Carnethon, M., et al.: Heart disease and stroke statistics 2009 update: a report from the American heart association statistics committee and stroke statistics subcommittee. Circulation 119(3), e21–e181 (2009)Google Scholar
  13. 13.
    Longo, L.: Argumentation for knowledge representation, conflict resolution, defeasible inference and its integration with machine learning. In: Holzinger, A. (ed.) Machine Learning for Health Informatics. LNCS (LNAI), vol. 9605, pp. 183–208. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-50478-0_9CrossRefGoogle Scholar
  14. 14.
    Longo, L., Dondio, P.: Defeasible reasoning and argument-based systems in medical fields: an informal overview. In: 2014 IEEE 27th International Symposium on Computer-Based Medical Systems, pp. 376–381, New York (2014)Google Scholar
  15. 15.
    Longo, L., Hederman, L.: Argumentation theory for decision support in health-care: a comparison with machine learning. In: Imamura, K., Usui, S., Shirao, T., Kasamatsu, T., Schwabe, L., Zhong, N. (eds.) BHI 2013. LNCS (LNAI), vol. 8211, pp. 168–180. Springer, Cham (2013).  https://doi.org/10.1007/978-3-319-02753-1_17CrossRefGoogle Scholar
  16. 16.
    Longo, L., Kane, B., Hederman, L.: Argumentation theory in health care. In: Proceedings of CBMS 2012, The 25th IEEE International Symposium on Computer-Based Medical Systems, Rome, Italy, 20–22 June 2012, pp. 1–6 (2012)Google Scholar
  17. 17.
    Matt, P.A., Morgem, M., Toni, F.: Combining statistics and arguments to compute trust. In: 9th International Conference on Autonomous Agents and Multiagent Systems, Toronto, Canada, vol. 1, pp. 209–216. ACM, May 2010Google Scholar
  18. 18.
    Prakken, H.: An abstract framework for argumentation with structured arguments. Argum. Comput. 1(2), 93–124 (2010)CrossRefGoogle Scholar
  19. 19.
    Rizzo, L., Longo, L.: Representing and inferring mental workload via defeasible reasoning: a comparison with the NASA task load index and the workload profile. In: 1st Workshop on Advances in Argumentation in Artificial Intelligence, pp. 126–140 (2017)Google Scholar
  20. 20.
    Rizzo, L., Majnaric, L., Dondio, P., Longo, L.: An investigation of argumentation theory for the prediction of survival in elderly using biomarkers. In: Iliadis, L., Maglogiannis, I., Plagianakos, V. (eds.) AIAI 2018. IAICT, vol. 519, pp. 385–397. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-92007-8_33CrossRefGoogle Scholar
  21. 21.
    Siler, W., Buckley, J.J.: Fuzzy Expert Systems and Fuzzy Reasoning. Wiley, Hoboken (2005)zbMATHGoogle Scholar
  22. 22.
    Strimbu, K., Tavel, J.A.: What are biomarkers? Curr. Opin. HIV AIDS 5(6), 463 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of ComputingDublin Institute of TechnologyDublinIreland
  2. 2.Department of Family Medicine, School of MedicineUniversity of OsijekOsijekCroatia

Personalised recommendations