Dynamic Bayesian networks as prognostic models for clinical patient management

Journal of Biomedical Informatics

Volumn 41, Issue 4, 2008, Pages 515-529

  van Gerven, Marcel A J ^a   Taal, Babs G ^b   Lucas, Peter J F ^a  

^a RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

^b ANTONI VAN LEEUWENHOEK HOSPITAL   (Netherlands)

Author keywords

Carcinoid tumor; Dynamic Bayesian network; Prognosis; Proportional hazards model

Indexed keywords

BAYESIAN NETWORKS; COMPUTER NETWORKS; DISTRIBUTED PARAMETER NETWORKS; DYNAMICS; FORECASTING; HEALTH; INFERENCE ENGINES; INTELLIGENT NETWORKS; LEARNING SYSTEMS; MARKOV PROCESSES; MECHANICS; METROPOLITAN AREA NETWORKS; NETWORK PROTOCOLS; PATIENT TREATMENT; SPEECH ANALYSIS;

DECISION RULES; DISEASE PROGRESSIONS; DOMAIN EXPERTS (DES); DYNAMIC BAYESIAN NETWORK (DBN); DYNAMIC BAYESIAN NETWORKS (DBNS); ELSEVIER (CO); INDIVIDUAL (PSS 544-7); MARKOV MODELLING; MEDICAL DOMAINS; PATIENT MANAGEMENT; PROPORTIONAL HAZARDS; PROPORTIONAL HAZARDS MODEL; TRADITIONAL METHODS;

DYNAMIC MODELS;

ADULT; AGED; ARTICLE; BAYES THEOREM; CARCINOID; CASE REPORT; HUMAN; MALE; MEDICAL INFORMATICS; MORTALITY; PATIENT CARE; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; PROPORTIONAL HAZARDS MODEL; SURVIVAL;

ALGORITHMS; BAYES THEOREM; CARCINOID TUMOR; DECISION SUPPORT SYSTEMS, CLINICAL; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; NEURAL NETWORKS (COMPUTER); PROGNOSIS; RISK ASSESSMENT; RISK FACTORS; SURVIVAL ANALYSIS; SURVIVAL RATE;

EID: 46649093156     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2008.01.006     Document Type: Article

References (50)

1. Lee K.L., Pryor D.B., Harrell F.E., Califf H.M., Behar V.S., Floyd W.L., et al. Predicting outcome in coronary disease: statistical models vs. expert clinicians. Am J Med 80 4 (1986) 553-560
2. Knaus W.A., Wagner D.P., and Lynn J. Short term mortality predictions for critically ill hospitalised adults: Science and ethics. Science 254 (1991) 389-394
3. Glare P., Virik K., Jones M., Hudson M., Eychmuller S., Simes J., et al. A systematic review of physicians' survival predictions in terminally ill cancer patients. Br Med J 327 7408 (2003) 195-198
4. Wyatt J.C., and Altman D.G. Prognostic models: clinically useful or quickly forgotten?. Br Med J 311 (1995) 1539-1541
5. Abu-Hanna A., and Lucas P.J.F. Prognostic models in medicine: AI and statistical approaches. Meth Inform Med 40 (2001) 1-5
6. Knaus W., Wagner D.P., Draper E., Zimmerman J., Bergner M., Bastos P., et al. The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalised adults. Chest 100 (1991) 1619-1636
7. Le Gall J.R., Lemeshow S.S., and Saulnier F. A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study. J Am Med Inform Assoc 270 (1993) 2957-2963
8. Groeger J.S., Lemeshow S., Price K., Nierman D.M., White Jr. P., Klar J., et al. Multicenter outcome study of cancer patients admitted to the intensive care unit: a probability of mortality model. J Clin Oncol 16 (1998) 761-770
9. Cox D.R., and Oakes D. Analysis of survival data (1984), Chapman & Hall, London, UK
10. Sonnenberg F.A., and Beck J.R. Markov models in medical decision making: a practical guide. Med Decis Making 13 (1993) 322-338
11. Cruz J.A., and Wishart D.S. Applications of machine learning in cancer prediction and prognosis. Cancer Inform 2 (2006) 59-78
12. Delen D., Walker G., and Kadam A. Predicting breast cancer survivability: a comparison of three data mining methods. Artif Intell Med 34 2 (2005) 113-127
13. Ohno-Machado L. A comparison of Cox proportional hazards and artificial neural network models for medical prognosis. Comput Biol Med 27 (1997) 55-65
14. Hart A., and Wyatt J.C. Evaluating black boxes as medical decision-aids: issues arising from a study of neural networks. Int J Med Inform 15 (1990) 229-236
15. Dean T., and Kanazawa K. A model for reasoning about persistence and causation. Comput Intell 5 3 (1989) 142-150
16. Zuetenhorst J.M., and Taal B.G. Metastatic carcinoid tumors: a clinical review. Oncologist 10 2 (2005) 123-131
17. Augusto J.C. Temporal reasoning for decision support in medicine. Artif Intell Med 33 (2005) 1-24
18. Long W.J. Temporal reasoning for diagnosis in a causal probabilistic knowledge base. Artif Intell Med 8 (1996) 193-215
19. Allen J.F. Towards a general theory of action and time. Artif Intell 23 (1984) 123-154
20. Charitos T, van der Gaag LC, Visscher S, Schurink K, Lucas PJF. A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients. In: Holmes JH, Peek N, editors. Proceedings of the tenth intelligent data analysis in medicine and pharmacology workshop; 2005. p. 32-7.
21. Dagum P., and Galper A. Forecasting sleep apnea with dynamic network models. In: Heckerman D., and Mamdani A. (Eds). Proceedings of the ninth conference on uncertainty in artificial intelligence (1993), Morgan Kaufman, San Francisco, CA 64-71
22. Andreassen S., Benn J.J., Hovorka R., Olesen K.G., and Carson E.R. A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study. Comput Meth Programs Biomed 41 (1994) 153-165
23. Hernando M.E., Gómez E.J., del Pozo F., and Corcoy R. DIABNET: a qualitative model-based advisory system for therapy planning in gestational diabetes. Int J Med Inform 21 4 (1996) 359-374
24. Druzdzel M.J. Five useful properties of probabilistic knowledge representations from the point of view of intelligent systems. Fundam Inform 30 3-4 (1997) 241-254
25. Pearl J. Causality: models, reasoning and inference (2000), Cambridge University Press, New York, NY
26. Kulikowski C.A., and Weiss S.M. Representation of expert knowledge for consultation: the CASNET and EXPERT projects. In: Szolovits P. (Ed). Artificial Intelligence in Medicine (1982), Westview Press, Boulder, CO 21-55
27. Patil R.S., Szolovits P., and Schwartz W.B. Modelling knowledge of the patient in acid-base and electrolyte disorders. In: Szolovits P. (Ed). Artificial Intelligence in Medicine (1982), Westview Press, Boulder, CO 191-226
28. Lucas P.J.F. Logic engineering in medicine. Knowl Eng Rev 10 2 (1995) 153-179
29. Teach R.L., and Shortliffe E.H. An analysis of physicians' attitudes. In: Buchanan B.G., and Shortliffe E.H. (Eds). Rule-based expert systems: the MYCIN experiments of the stanford heuristic programming project (1984), Addison-Wesley, Reading, Mass 635-652
30. Lacave C., and Díez F.J. A review of explanation methods for Bayesian networks. Knowl Eng Rev 17 2 (2002) 107-127
31. Murphy K.P. Dynamic Bayesian networks (2002), UC Berkeley, Berkeley, CA
32. van Gerven M.A.J., and Taal B.G. Structure and parameters of a Bayesian network for carcinoid prognosis (2006), Radboud University Nijmegen, Nijmegen, The Netherlands ICIS-R6033
33. van Gerven M.A.J. Bayesian networks for clinical decision support (2007), Radboud University Nijmegen, Nijmegen, the Netherlands
34. Taal B.G., and Smits M. Developments in diagnosis and treatment of metastatic midgut carcinoid tumours. Minerva Gastroenterol 51 (2005) 335-344
35. Capella C., Heitz P.U., Hofler H., Solcia E., and Kloppel G. Revised classification of neuroendocrine tumours of the lung, pancreas and gut. Virchows Arch 425 6 (1995) 547-560
36. Oken M.M., Creech R.H., Tormey D.C., Horton J., Davis T.E., McFadden E.T., et al. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol 5 (1982) 649-655
37. Good I.J. Good thinking: the foundations of probability and its applications (1983), University of Minnesota Press, Mineapolis, MN
38. Henrion M. Some practical issues in constructing belief networks. In: Lemmer J.F., Levitt T., and Kanal L.N. (Eds). Proceedings of the third conference on uncertainty in artificial intelligence (1989), Elsevier Science, New York, NY 161-173
39. Centraal Bureau voor de Statistiek. Overlevingstafels naar Geslacht. CBS Periodiek; 2005.
40. 131I-labelled MIBG and the combination. Eur J Gastroenterol Hepatol 11 (1999) 1157-1164
41. Sutton R., Doran H.E., Williams E.M.I., Vora J., Vinjamuri S., Evans J., et al. Surgery for midgut carcinoids. Endocr Relat Cancer 10 (2003) 469-481
42. Nobels F.R.E., Kwekkeboom D.J., Bouillon R., and Lamberts S.W.J. Chromogranin A: its clinical values as marker of endocrine tumours. Eur J Clin Invest 28 (1998) 431-440
43. Herbomez M.D., and Gouze V. Chromogranin: a marker of neuroendocrine tumours. Ann Biol Clin 60 (2002) 641-646
44. Boutilier C., Friedman N., Goldszmidt M., and Koller D. Context-specific independence in Bayesian networks. In: Horvitz E., and Jensen F. (Eds). Proceedings of the twelfth conference on uncertainty in artificial intelligence (1996), Morgan Kaufman, San Francisco, CA 115-123
45. Taal B.G., Hoefnagel C., Boot H., Jong D.D., and Rutgers M. Carcinoide tumoren van de darm: ontwikkelingen binnen Nederland in diagnostiek en palliatieve behandeling. Ned Tijdschr Geneeskd 143 9 (1999) 445-451
46. Zuetenhorst J.M., Bonfrer J.M.G., Korse C.M., Bakker R.H., 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
47. Zuetenhorst J.M., Korse C.M., Bonfrer J.M.G., Bakker R.H., and Taal B.G. The role of natriuretic peptides in the diagnosis and treatment of patients with carcinoid heart disease. Br J Cancer 90 (2004) 2073-2079
48. Tversky A., and Kahneman D. Availability: a heuristic for judging frequency and probability. Cognit Psychol 5 (1973) 207-232
49. Atkinson R.C., and Shiffrin R.M. The control of short-term memory. Sci Am 225 (1971) 82-90
50. Leong T.Y. An integrated approach to dynamic decision making under uncertainty (1994), Massachusetts Institute of Technology, Cambridge, MA