Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems

Artificial Intelligence in Medicine

Volumn 57, Issue 3, 2013, Pages 197-206

  Oniśko, Agnieszka ^a,b   Druzdzel, Marek J ^a,c  

^a BIALYSTOK UNIVERSITY OF TECHNOLOGY   (Poland)

^b UNIVERSITY OF PITTSBURGH   (United States)

^c UNIVERSITY OF PITTSBURGH   (United States)

Author keywords

Bayesian networks; Medical diagnostic systems; Probability elicitation; Sensitivity analysis

Indexed keywords

BAYESIAN NETWORK MODELS; DIAGNOSTIC ACCURACY; MACHINE LEARNING REPOSITORY; MEDICAL DATA SETS; MEDICAL DIAGNOSTICS; METHODS AND MATERIALS; MODEL PARAMETERS; NUMERICAL PARAMETERS;

DIAGNOSIS; KNOWLEDGE ENGINEERING; LIVER; SENSITIVITY ANALYSIS;

BAYESIAN NETWORKS;

ALGORITHM; ARTICLE; BAYES THEOREM; BAYESIAN LEARNING; CANCER DIAGNOSIS; CANCER PROGNOSIS; DIAGNOSTIC ACCURACY; HEPAR II MODEL; HUMAN; INTENSIVE CARE; IRVINE MACHINE LEARNING REPOSITORY MODEL; LIVER DISEASE; MACHINE LEARNING; MEDICAL DECISION MAKING; MEDICAL INFORMATION SYSTEM; MODEL; MULTIPLE ORGAN FAILURE; PATIENT CARE; PRIORITY JOURNAL; SENSITIVITY ANALYSIS;

BAYES THEOREM; DECISION SUPPORT TECHNIQUES; DIAGNOSIS; HUMANS; LIVER DISEASES; PROBABILITY;

EID: 84876725346     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2013.01.004     Document Type: Article

References (45)

1. Henrion M., Breese J.S., Horvitz E.J. Decision analysis and expert systems. AI Magazine 1991, 12(4):64-91.
2. Pearl J. Probabilistic reasoning in intelligent systems: networks of plausible inference 1988, Morgan Kaufmann Publishers, Inc., San Mateo, CA.
3. Pradhan M., Henrion M., Provan G., del Favero B., Huang K. The sensitivity of belief networks to imprecise probabilities: an experimental investigation. Artificial Intelligence 1996, 85(1-2):363-397.
4. Oniśko A., Druzdzel M.J. Effect of imprecision in probabilities on Bayesian network models: an empirical study. Working Notes of the European Conference on Artificial Intelligence in Medicine (AIME-03) Workshop on Qualitative and Model-Based Reasoning in Biomedicine 2003, 45-49. P.J. Lucas (Ed.).
5. Druzdzel M.J., Oniśko A. The impact of overconfidence bias on practical accuracy of Bayesian network models: an empirical study. Working Notes of the 2008 Bayesian Modelling Applications Workshop, Special Theme: How Biased are our Numbers? Annual Conference on Uncertainty in Artificial Intelligence (UAI-2008) 2008, S. Renooij, H.J. Tabachneck-Schijf, S.M. Mahoney (Eds.).
6. Morgan M.G., Henrion M. Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis 1990, Cambridge University Press, Cambridge.
7. van der Gaag L.C., Renooij S. Analysing sensitivity data from probabilistic networks. Uncertainty in Artificial Intelligence: Proceedings of the Sixteenth Conference (UAI-2001) 2001, 530-537. Morgan Kaufmann Publishers, San Francisco, CA.
8. Chan H., Darwiche A. When do numbers really matter?. Journal of Artificial Intelligence Research 2002, 17:265-287.
9. Coupé V.H.M., Peek N., Ottenkamp J., Habbema J.D.F. Using sensitivity analysis for efficient quantification of a belief network. Artificial Intelligence in Medicine 1999, 17(3):223-247.
10. Gorry G.A., Barnett G.O. Experience with a model of sequential diagnosis. Computer and Biomedical Research 1968, 1(5):490-507.
11. Ledley R.S., Lusted L.B. Reasoning foundations of medical diagnosis. Science 1959, 130(3366):9-21.
12. Cooper GF. NESTOR: a computer-based medical diagnostic aid that integrates causal and probabilistic knowledge, Ph.D. thesis, Stanford University, Computer Science Department; 1984.
13. Andreassen S., Woldbye M., Falck B., Andersen S.K. MUNIN - a causal probabilistic network for interpretation of electromyographic findings. Proceedings of the Tenth International Joint Conference on Artificial Intelligence 1987, 366-372. Morgan Kaufmann Publishers, Los Altos, CA. J. McDermott (Ed.).
14. Beinlich I., Suermondt H., Chavez R., Cooper G. The ALARM monitoring system: a case study with two probabilistic inference techniques for belief networks. Proceedings of the Second European Conference on Artificial Intelligence in Medical Care 1989, 247-256. J. Hunter, J. Cookson, J. Wyatt (Eds.).
15. Heckerman D.E., Horvitz E.J., Nathwani B.N. Toward normative expert systems: Part I - The Pathfinder project. Methods of Information in Medicine 1992, 31:90-105.
16. Shwe M., Middleton B., Heckerman D., Henrion M., Horvitz E., Lehmann H., et al. Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base: I. The probabilistic model and inference algorithms. Methods of Information in Medicine 1991, 30(4):241-255.
17. Díez F.J., Mira J., Iturralde E., Zubillaga S. DIAVAL, a Bayesian expert system for echocardiography. Artificial Intelligence in Medicine 1997, 10:59-73.
18. van der Gaag L., Renooij S., Witteman C., Aleman B., Taal B. Probabilities for a probabilistic network: a case study in oesophageal cancer. Artificial Intelligence in Medicine 2002, 25:123-148.
19. Lucas P.J.F., de Bruijn N., Schurink K., Hoepelman A. A probalistic and decision-theoretic approach to the management of infectious disease at the ICU. Artificial Intelligence in Medicine 2000, 19(3):251-279.
20. Lucas P.J.F., van der Gaag L., Abu-Hanna A. Bayesian networks in biomedicine and health-care. Artificial Intelligence in Medicine 2004, 30:201-214.
21. Leong T.-Y. Multiple perspective dynamic decision making. Artificial Intelligence 1998, 105:209-261.
22. Cao C., Leong T.-Y., Leong A.P.K., Seow F.C. Dynamic decision analysis in medicine: a data-driven approach. International Journal of Medical Informatics 1998, 51:13-28.
23. van Gerven M.A.J., Taal B.G., Lucas P.J.F. Dynamic Bayesian networks as prognostic models for clinical patient management. Journal of Biomedical Informatics 2008, 41:515-529.
24. Galan S.F., Aguado F., Díez F.J., Mira J. NasoNet modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time. Artificial Intelligence in Medicine 2002, 25:247-264.
25. Ferrazzi F., Sebastiani P., Kohane I.S., Ramoni M., Bellazzi R. Dynamic Bayesian networks in modelling cellular systems: a critical appraisal on simulated data. Proceedings of the 19th IEEE international symposium on computer-based medical systems (CBMS 2006) 2006, 544-549. IEEE Computer Society, Salt Lake City, Utah, USA.
26. Peelen L., de Keizer N., Jonge E., Bosman R., Abu-Hanna A., Peek N. Using hierarchical dynamic Bayesian networks to investigate dynamics of organ failure in patients in the intensive care unit. Journal of Biomedical Informatics 2010, 43:273-286.
27. Mosteller F., Youtz C., Zahn D. The distribution of sums of rounded percentages. Demography 1967, 4(2):850-858.
28. Balinski M.L., Young H.P. Fair representation. Meeting the Ideal of one man, one vote 1982, Yale University Press, New Haven, CT.
29. Heinrich L., Pukelsheim F., Schwingenschlogl U. On stationary multiplier methods for the rounding of probabilities and the limiting law of the Sainte-Lague divergence. Statistics and Decisions 2005, 23:117-129.
30. Oniśko A., Druzdzel M.J., Wasyluk H. Learning Bayesian network parameters from small data sets: application of Noisy-OR gates. International Journal of Approximate Reasoning 2001, 27(2):165-182.
31. Moore A.W., Lee M.S. Efficient algorithms for minimizing cross validation error. Proceedings of the 11th International Conference on Machine Learning 1994, 190-198. Morgan Kaufmann, San Francisco.
32. Oniśko A., Druzdzel M.J., Wasyluk H. An experimental comparison of methods for handling incomplete data in learning parameters of Bayesian networks. Intelligent information systems, Advances in soft computing series 2002, 351-360. Physica-Verlag (A Springer-Verlag Company), Heidelberg. M. Kłopotek, M. Michalewicz, S. Wierzchoń (Eds.).
33. Oniśko A. Evaluation of the Hepar II system for diagnosis of liver disorders. Working Notes of the Workshop Bayesian Models in Medicine, European Conference on Artificial Intelligence in Medicine (AIME-01) 2001, 59-64. P. Lucas, L.C. van der Gaag, A. Abu-Hanna (Eds.).
34. Oniśko A., Druzdzel M.J., Wasyluk H. Extension of the Hepar II model to multiple-disorder diagnosis. Intelligent information systems, Advances in soft computing series 2000, 303-313. Physica-Verlag (A Springer-Verlag Company), Heidelberg. M. Kłopotek, M. Michalewicz, S. Wierzchoń (Eds.).
35. Czerniak J., Zarzycki H. Application of rough set in the presumptive diagnosis of urinary system diseases. Artificial Intelligence and Security in Computing Systems, ACS'2002 9th International Conference 2003, 41-51. Kluwer Academic Publishers, Norwell, MA, USA. J. Soldek, L. Drobiazgiewicz (Eds.).
36. Kurgan L.A., Cios K.J., Tadeusiewicz R., Ogiela M.R., Goodenday L.S. Knowledge discovery approach to automated cardiac SPECT diagnosis. Artificial Intelligence in Medicine 2001, 23(2):149-169.
37. de Campos A., Bernardes J., Garrido A., Marques-de Sá J., Pereira-Leite L. Sisporto 2.0 a program for automated analysis of cardiotocograms. Journal of Maternal-Fetal Medicine 2000, 9(5):311-318.
38. Cestnik B., Kononenko I., Bratko I. Assistant 86: a knowledge-elicitation tool for sophisticated users. Progress in machine learning 1987, 31-45. I. Bratko, N. Lavrac (Eds.).
39. Kononenko I. Inductive and Bayesian learning in medical diagnosis. Applied Artificial Intelligence 1993, 7:317-337.
40. Druzdzel M.J. SMILE: structural modeling, inference, and learning engine and GeNIe: a development environment for graphical decision-theoretic models. Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99) 1999, 902-903. AAAI Press/The MIT Press, Menlo Park, CA. J. Hendler, D. Subramanian (Eds.).
41. Clancey W.J., Cooper G. Uncertainty and evidential support. Buchanan and Shortliffe [42] 1984, pp. 209-232 (Chapter 10).
42. Rule-based expert systems: the MYCIN experiments of the Stanford heuristic programming project 1984, Addison-Wesley, Reading, MA. B.G. Buchanan, E.H. Shortliffe (Eds.).
43. Likert R. A technique for the measurement of attitudes. Archives of Psychology 1932, 22(140):1-55.
44. Diez F.J., Druzdzel M.J. Verbal expressions of probability. Encyclopedia of Medical Decision Making 2009, 53-57. Sage Publications, Thousand Oaks, CA. M.W. Kattan (Ed.).
45. Oniśko A., Druzdzel M.J. Impact of quality of Bayesian network parameters on accuracy of medical diagnostic systems. Working Notes of the 2011 AIME-11 Workshop on Probabilistic Problem Solving in Biomedicine (ProBioMed-11), in Conjunction with the Thirteenth Conference on Artificial Intelligence in Medicine (AIME-2011) 2011, 135-148. A. Hommersom, P.J. Lucas (Eds.).