Advertisement

Combining Argumentation and Bayesian Nets for Breast Cancer Prognosis

  • Matt Williams
  • Jon Williamson
Article

Abstract

We present a new framework for combining logic with probability, and demonstrate the application of this framework to breast cancer prognosis. Background knowledge concerning breast cancer prognosis is represented using logical arguments. This background knowledge and a database are used to build a Bayesian net that captures the probabilistic relationships amongst the variables. Causal hypotheses gleaned from the Bayesian net in turn generate new arguments. The Bayesian net can be queried to help decide when one argument attacks another. The Bayesian net is used to perform the prognosis, while the argumentation framework is used to provide a qualitative explanation of the prognosis.

Key words

argumentation logic bayes theorem bayesian networks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Amgoud, L., Cayrol, C., and Lagasquie-Schiex, M.-C., 2004, “On Bipolarity in Argumentation frameworks,” in: 10th International Workshop on Non-Monotonic Reasoning (NMR 2004) J.P. Delgrande and T. Schaub eds., Whistler, Canada, June 6–8, 2004, Proceedings. pp. 1–9.Google Scholar
  2. A/S, H.E.: 1989, ‘Hugin’Google Scholar
  3. Borak, J. and Veilleux, S., 1982, “Errors of intuitive logic among physicians,” Soc. Sci. Med. 16, 1939–1947.CrossRefGoogle Scholar
  4. Clark, P. and Niblett, T., 1987, “Induction in Noisy Domains,” in: Proceedings of the 2nd European Working Session on Learning. Bled Yugoslavia:. Sigma Press.Google Scholar
  5. Cristofanilli, M., Hayes, D., Budd, G., Ellis, M., Stopeck, A., Reuben, J., Doyle, G., Matera, J., Allard, W., Miller, M., Fritsche, H., Hortobagyi, G., and Terstappen, L., 2005, “Circulating tumor cells: A novel prognostic factor for newly diagnosed metastatic breast cancer,” J Clin Oncol 23, 1420–1430.CrossRefGoogle Scholar
  6. Dung, P., 1995, “On the Acceptability of Arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games,” Artificial Intelligence 77, 321–357.CrossRefGoogle Scholar
  7. Fox, J. and Parsons, S., 1997, “On Using Arguments For Reasoning About Actions And Values,” in: Proc AAAI Spring Symposium on Qualitative Preferences in Deliberation and Practical Reasoning, Stanford.Google Scholar
  8. Franklin, B., 1887, Collected Letters, Putnam New York.Google Scholar
  9. Friedman, 2004, “Inferring cellular networks using probabilistic graphical models,” Science 303, 799–805.CrossRefGoogle Scholar
  10. Galea, M., Blamey, R., Elston, C.E., and Ellis, I., 1992, “The Nottingham Prognostic Index in primary breast cancer,” Breast Cancer Research and Treatment 3, 207–219.CrossRefGoogle Scholar
  11. Gard, R., 1961, Buddhism, George Braziller Inc New York.Google Scholar
  12. Hunter, A. and Besnard, P., 2001, “A logic-based theory of deductive arguments,” Artificial Intelligence 128, 203–235.CrossRefGoogle Scholar
  13. Kahneman, D. and Tversky, A., 1973, “On the psychology of prediction,” Psychol. Rev. 80, 237–251.CrossRefGoogle Scholar
  14. Korb, K.B. and Nicholson, A.E., 2003, Bayesian Artificial Intelligence, London: Chapman and Hall / CRC Press.Google Scholar
  15. Krause, P., Ambler, S., Elvang-Goranssan, M. and Fox, J., 1995, “A logic of argumentation for reasoning under uncertainty,” Computational Intelligence 11, 113–131.Google Scholar
  16. McConachy, R., Korb, K.B., and Zukerman, I., 1998, “A Bayesian approach to automating argumentation,” in Proceedings of New Methods in Language Processing & Computational Natural Language Learning (NeMLaP3/CoNLL98) D.M.W. Powers ed., pp. 91–100.Google Scholar
  17. McPherson, K., Steel, C., and Dixon, J.C., 2000, “Breast cancer: Epidemiology. Risk factors and Genetics,” BMJ 321, 624–628.CrossRefGoogle Scholar
  18. Michalski, R., Mozetic, I., Hong, J., and Lavrac, N., 1986, “The Multi-Purpose Incremental Learning System AQ15 and its Testing: Application to Three Medical Domains,” in: Proceedings of the Fifth National Conference on Artificial Intelligence, Philadelphia PA, pp. 1041–1045.Google Scholar
  19. Parsons, S., 2003, “Order of magnitude reasoning and qualitative probability,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11(3), 373–390.CrossRefGoogle Scholar
  20. Parsons, S., 2004, “On precise and correct qualitative probabilistic reasoning,” International Journal of Approximate Reasoning 35, 111–135.CrossRefGoogle Scholar
  21. Pearl, J., 2000, Causality: Models, Reasoning, and Inference, Cambridge: Cambridge University Press.Google Scholar
  22. Pollock, J.L., 1999, “Rational Cognition in OSCAR,” in: ATAL, pp. 71–90.Google Scholar
  23. Poole, D., 2002, “Logical argumentation, abduction, and Bayesian decision theory: A Bayesian approach to logical arguments and its application to legal evidential reasoning,” Cardozo Law Review 22, 1733–1745.Google Scholar
  24. Prakken, H. and Sartor, G., 1996, “Argument-based extended logic programming with defeasible priorities,” in Working Notes of 3rd ModelAge Workshop: Formal Models of Agents, P.-Y. Schobbens, ed., Sesimbra, Portugal.Google Scholar
  25. Quinn, M. and Allen, E., 1995, “Changes in incidence of and mortality from breast cancer in England and Wales since introduction of screening,” BMJ 311, 1391–1395.Google Scholar
  26. Richards, M., Smith, I., and Dixon, J., 1994, “Role of systemic treatment for primary operable breast cancer,” BMj 309, 1263–1366.Google Scholar
  27. Ries, L., Eisner, M., Kosary, C., Hankey, B., Miller, B., Clegg, L., Mariotto, A., Feuer, E., and Edwards, B., 2004, SEER Cancer Statistics Review 1975-2001, National Cancer Institute.Google Scholar
  28. Saha, S. and Sen, S., 2004, “A Bayes Net approach to Argumentation,” in: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS'04), Vol 3, 1436–1437.Google Scholar
  29. Spirtes, P., Glymour, C., and Scheines, R., 1993, Causation, Prediction, and Search, Cambridge MA: MIT Press, second (2000) edition.Google Scholar
  30. Sutton, D. and Fox, J., 2003, “The syntax and semantics of PROforma,” J Am Med Inform Assoc. 10(5), 433–443.CrossRefGoogle Scholar
  31. Veer, L., Paik, S., and Hayes, D., 2005, “Gene expression profiling of breast cancer: A new tumor marker,” J Clin Oncol. 23, 1631–1635.CrossRefGoogle Scholar
  32. Williamson, J., 2005, Bayesian nets and Causality: Philosophical and Computational Foundations, Oxford: Oxford University Press.Google Scholar
  33. Wittig, F. and Jameson, A., 2000, “Exploiting Qualitatve Knowledge in the Learning of Conditional Probabilites of Bayesian Networks,” In: C. Boutilier and M. Goldszmidt (eds.): Uncertainty in Artificial Intelligence: Proceedings of the Sixteenth Conference.Google Scholar
  34. Zwitter, M. and Soklic, M., 1988, “Breast Cancer Characteristics and Recurrence Data.”Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Advanced Computation Laboratory, Cancer ResearchLondonUK
  2. 2.Department of PhilosophyLogic and Scientific Method, London School of EconomicsLondonUK

Personalised recommendations