Predicting carcinoid heart disease with the noisy-threshold classifier

Artificial Intelligence in Medicine

Volumn 40, Issue 1, 2007, Pages 45-55

  van Gerven, Marcel A J ^a   Jurgelenaite, Rasa ^a   Taal, Babs G ^b   Heskes, Tom ^a   Lucas, Peter J F ^a  

^a RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

^b ANTONI VAN LEEUWENHOEK HOSPITAL   (Netherlands)

Author keywords

Bayesian classification; Carcinoid heart disease; Causal independence; Noisy threshold model

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION TREES; DISEASES; LEARNING ALGORITHMS; PATIENT TREATMENT; REGRESSION ANALYSIS; TUMORS;

BAYESIAN CLASSIFICATION; CARCINOID HEART DISEASE; CAUSAL INDEPENDENCE; NOISY-THRESHOLD MODEL;

CARDIOLOGY;

ARTICLE; BAYES THEOREM; CARCINOID SYNDROME; CLINICAL FEATURE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; HOSPITAL ADMISSION; HUMAN; LIVER METASTASIS; MAJOR CLINICAL STUDY; MEDICAL DECISION MAKING; MIDGUT; NOISE MEASUREMENT; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; RISK FACTOR; STOMACH CARCINOID;

ALGORITHMS; AREA UNDER CURVE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CARCINOID HEART DISEASE; DECISION SUPPORT TECHNIQUES; DECISION TREES; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; LOGISTIC MODELS; MODELS, STATISTICAL; PREDICTIVE VALUE OF TESTS; PROGNOSIS; ROC CURVE;

EID: 34247544962     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2006.09.003     Document Type: Article

References (41)

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