Artificial Intelligence in Medicine
Volume 40, Issue 1 , Pages 45-55 , May 2007

Predicting carcinoid heart disease with the noisy-threshold classifier

  • Marcel A.J. van Gerven

      Affiliations

    • Institute for Computing and Information Sciences, Radboud University Nijmegen, Toernooiveld 1, 6525 ED Nijmegen, The Netherlands
    • Corresponding Author InformationCorresponding author. Tel.: +31 24 365 34 56; fax: +31 24 365 33 56.
  • ,
  • Rasa Jurgelenaite

      Affiliations

    • Institute for Computing and Information Sciences, Radboud University Nijmegen, Toernooiveld 1, 6525 ED Nijmegen, The Netherlands
  • ,
  • Babs G. Taal

      Affiliations

    • Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
  • ,
  • Tom Heskes

      Affiliations

    • Institute for Computing and Information Sciences, Radboud University Nijmegen, Toernooiveld 1, 6525 ED Nijmegen, The Netherlands
  • ,
  • Peter J.F. Lucas

      Affiliations

    • Institute for Computing and Information Sciences, Radboud University Nijmegen, Toernooiveld 1, 6525 ED Nijmegen, The Netherlands

Received 23 June 2006 ,Revised 20 September 2006 ,Accepted 26 September 2006.

References 

  1. Pearl J. Probabilistic reasoning in intelligent systems: networks of plausible inference. 2nd edition. San Francisco, CA: Morgan Kaufmann; 1988;
  2. Ledley R, Lusted L. Reasoning foundation of medical diagnosis: symbolic logic, probability, and value theory aid our understanding of how physicians reason. Science. 1959;130:9–21
  3. de Dombal FT, Leaper D, Staniland J, Horrocks J, McCann A. Computer aided diagnosis of acute abdominal pain. Br Med J. 1972;2:9–13
  4. Spiegelhalter D, Knill-Jones R. Statistical and knowledge-based approaches to clinical decision-support systems, with an application in gastroenterology. J R Stat Soc. 1984;147:35–77
  5. Sahami M. Learning limited dependence Bayesian classifiers. In: Second international conference on knowledge discovery in databases. Portland, OR: AAAI Press; 1996;p. 335–338
  6. Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers. Machine Learn. 1997;29:131–163
  7. Cheng J, Greiner R. Comparing Bayesian network classifiers. In: Proceedings of the fifteenth conference on uncertainty in artificial intelligence. Stockholm: Morgan Kaufmann; 1999;p. 101–107
  8. Domingos P, Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learn. 1997;29:103–130
  9. Teach R, Shortliffe E. An analysis of physician attitudes regarding computer-based clinical consultation systems. Comput Biomed Res. 1981;14:542–558
  10. Lacave C, Díez F. A review of explanation methods for Bayesian networks. Knowledge Eng Rev. 2002;17(2):107–127
  11. Jurgelenaite R, Heskes T. EM algorithm for symmetric causal independence models. In: Proceedings of the seventeenth European conference on machine learning. Heidelberg, Germany: Springer-Verlag; 2006;p. 234–245
  12. Heckerman D, Breese J. A new look at causal independence. In: Proceedings of the tenth conference on uncertainty in artificial intelligence. San Francisco, CA: Morgan Kaufmann; 1994;p. 286–292
  13. Vomlel J. Exploiting functional dependence in Bayesian network inference. In: Proceedings of the eighteenth conference on uncertainty in artificial intelligence. San Francisco, CA: Morgan Kaufmann; 2002;p. 528–535
  14. Zuetenhorst J, Bonfrer J, Korse C, Bakker R, van Tinteren H, Taal BG. Carcinoid heart disease: the role of urinary 5-HIAA excretion and plasma levels of TGF- and FGF. Cancer. 2003;97:1609–1615
  15. Zhang N, Poole D. Exploiting causal independence in Bayesian network inference. J Artif Intell Res. 1996;5:301–328
  16. Díez F. Parameter adjustment in Bayes networks. The generalized noisy OR-gate. In: Proceedings of the ninth conference on uncertainty in artificial intelligence. San Francisco, CA: Morgan Kaufmann; 1993;p. 99–105
  17. Lucas P. Bayesian network modelling by qualitative patterns. Artif Intell. 2005;163:233–263
  18. Pradham M, Provan G, Middleton B, Henrion M. Knowledge engineering for large belief networks. In:  de Mantaras RL,  Poole D editor. Proceedings of the tenth conference on uncertainty in artificial intelligence. San Fransisco, CA: Morgan Kaufmann; 1994;p. 484–490
  19. Reiter R. On closed-world data bases. In:  Gallaire H,  Minker J editor. Logic and databases. New York, NY: Plenum Press; 1978;p. 55–76
  20. Enderton H. A mathematical introduction to logic. New York, NY: Academic Press, Inc.; 1972;
  21. Wegener I. The complexity of boolean functions. New York, NY: John Wiley & Sons; 1987;
  22. Jurgelenaite R, Heskes T, Lucas P. Noisy threshold functions for modelling causal independence in Bayesian networks. Tech. Re ICIS-R06014. Nijmegen, The Netherlands: Radboud University; 2006.
  23. Dempster A, Laird N, Rubin D. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc. 1977;39:1–38
  24. Edwards A. The meaning of binomial distribution. Nature. 1960;186:1074–1076
  25. Cam LL. An approximation theorem for the Poisson binomial distribution. Pacific J Math. 1960;10:1181–1197
  26. Zuetenhorst J, Taal B. Metastatic carcinoid tumors: a clinical review. Oncologist. 2005;10(2):123–131
  27. Zuetenhorst J, Taal B. Carcinoid heart disease. New Engl J Med. 2003;348:2359–2361
  28. van Rijsbergen C. Information retrieval. 2nd edition. London, UK: Butterworths; 1979;
  29. Kline R. Principles and practice of structural equation modeling. New York, NY: Guilford; 1998;
  30. Rubin D. Multiple imputation for nonresponse in surveys. New York, NY: Wiley; 1987;
  31. Witten I, Frank E. Data mining: practical machine learning tools and techniques. 2nd edition. San Francisco, CA: Morgan Kaufmann; 2005;
  32. Salzberg S. On comparing classifiers: pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery. 1997;1:317–327
  33. Egan J. Signal detection theory and ROC analysis. New York, NY: Academic Press; 1975;
  34. Bamber D. The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. J Math Psychol. 1975;12:387–415
  35. Cortes C, Mohri M. AUC optimization vs. error rate minimization. In:  Thrun S,  Saul L,  Schölkopf B editor. Advances in neural information processing systems, vol. 16. Cambridge, MA: MIT Press; 2004;
  36. Kohavi R, Wolpert DH. Bias plus variance decomposition for zero-one loss functions. In:  Saitta L editors. Machine learning: proceedings of the thirteenth international conference. San Mateo, CA: Morgan Kaufmann; 1996;p. 275–283
  37. Nobels F, Kwekkeboom D, Bouillon R, Lamberts S. Chromogranin A: its clinical values as marker of endocrine tumours. Eur J Clin Invest. 1998;28:431–440
  38. Modlin I, Shapiro M, Kidd M. Carcinoid tumors and fibrosis: an association with no explanation. Am J Gastroenterol. 2004;99:2466–2478
  39. van Gerven M, Lucas P. Using background knowledge to construct Bayesian classifiers for data-poor domains. In:  Bramer M,  Coenen F,  Allen T editor. Proceedings of AI-2004, the twenty-fourth SGAI international conference on innovative techniques and applications of artificial intelligence. London, UK: Springer-Verlag; 2004;p. 269–282
  40. Galán S, Díez F. Networks of probabilistic events in discrete time. Int J Approx Reason. 2002;30:181–202
  41. Galán S, Aguado F, Díez F, Mira J. Nasonet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time. Artif Intell Med. 2002;25(3):247–264

PII: S0933-3657(06)00140-0

doi: 10.1016/j.artmed.2006.09.003

Artificial Intelligence in Medicine
Volume 40, Issue 1 , Pages 45-55 , May 2007