Selecting treatment strategies with dynamic limited-memory influence diagrams

Artificial Intelligence in Medicine

Volumn 40, Issue 3, 2007, Pages 171-186

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

^a RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

^b UNIVERSIDAD NACIONAL DE EDUCACIÓN A DISTANCIA   (Spain)

^c ANTONI VAN LEEUWENHOEK HOSPITAL   (Netherlands)

Author keywords

Carcinoid tumors; Limited memory influence diagrams; Partially observable Markov decision processes; Planning; Simulated annealing

Indexed keywords

CARCINOID TUMORS; LIMITED-MEMORY INFLUENCE DIAGRAMS; PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES; TREATMENT STRATEGIES;

DECISION MAKING; EXPERT SYSTEMS; MARKOV PROCESSES; ONCOLOGY; SIMULATED ANNEALING; STRATEGIC PLANNING;

PATIENT TREATMENT;

ALGORITHM; ARTICLE; CANCER THERAPY; CARCINOID; DECISION MAKING; HEALTH CARE POLICY; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MEDICAL PRACTICE; PATHOPHYSIOLOGY; PHYSICIAN; PRIORITY JOURNAL; SIMULATION; THEORETICAL MODEL; TREATMENT INDICATION;

ALGORITHMS; CARCINOID TUMOR; DECISION SUPPORT TECHNIQUES; MARKOV CHAINS; QUALITY-ADJUSTED LIFE YEARS; STOCHASTIC PROCESSES;

EID: 34447254272     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2007.04.004     Document Type: Article

References (54)

1. Von Neumann J., and Morgenstern O. Theory of games and economic behavior (1947), Princeton University Press, Princeton, NJ
2. Zuetenhorst J.M., and Taal B.G. Metastatic carcinoid tumors: a clinical review. Oncologist 10 2 (2005) 123-131
3. Magni P., and Bellazzi R. DT-planner: an environment for managing dynamic decision problems. Comput Meth Programs Biomed 54 (1997) 183-200
4. Dean T., and Wellmann M. Planning and control (1991), Morgan Kaufmann, San Mateo, CA
5. Boutilier C., Dean T., and Hanks S. Planning under uncertainty: structural assumptions and computational leverage. In: Ghallab M., and Milani A. (Eds). New directions in AI planning (1996), IOS Press, Amsterdam, The Netherlands 157-171
6. Howard R.A. Dynamic programming and Markov processes (1960), The MIT Press, Cambridge, MA
7. Grimmett G.R., and Stirzaker D.R. Probability and random processes (1992), Clarendon Press, Oxford, UK
8. Sonnenberg F.A., and Beck J.R. Markov models in medical decision making: a practical guide. Med Decis Making 13 (1993) 322-338
9. Kuntz K.M., and Weinstein M.C. Modelling in economic evaluation. In: Drummond M., and McGuire A. (Eds). Economic evaluation in health care: merging theory with practice (2001), Oxford University Press, New York, NY 141-171
10. Bellman R.E. Dynamic programming (1957), Princeton University Press, Princeton, NJ
11. Aström K.J. Optimal control of Markov decision processes with incomplete state estimation. J Math Anal Appl 10 (1965) 174-205
12. Monahan G.E. A survey of partially observable Markov decision processes. Manag Sci 28 1 (1982) 1-16
13. Smallwood R.D., and Sondik E.J. The optimal control of partially observable Markov processes over a finite horizon. Oper Res 21 5 (1973) 1071-1088
14. Smallwood R.D., and Sondik E.J. The optimal control of partially observable Markov processes over the infinite horizon: discounted costs. Oper Res 26 2 (1978) 282-304
15. Papadimitriou C.H., and Tsitsiklis J.N. The complexity of Markov decision processes. Math Oper Res 12 3 (1987) 441-450
16. Lusena C., Goldsmith J., and Mundhenk M. Nonapproximability results for partially observable Markov decision processes. Artif Intell 14 (2001) 83-103
17. Tatman J.A., and Shachter R.D. Dynamic programming and influence diagrams. IEEE Trans Syst Man Cybern 20 2 (1990) 365-379
18. In: Howard R.A., and Matheson J.E. (Eds). Readings in the principles and applications of decision analysis (1984), Strategic Decisions Group, Menlo Park, CA
19. Cowell R., Dawid A.P., Lauritzen S.L., and Spiegelhalter D. Probabilistic networks and expert systems (1999), Springer, New York, NY
20. Pearl J. Causality: models, reasoning and inference (2000), Cambridge University Press, New York, NY
21. Druzdzel M.J. Five useful properties of probabilistic knowledge representations from the point of view of intelligent systems. Fundamenta Informaticae 30 3-4 (1997) 241-254
22. Owens D.K., Shachter R.D., and Nease R.F. Representation and analysis of medical decision problems with influence diagrams. Med Decis Making 17 3 (1997) 241-262
23. Leong, T-Y. An integrated approach to dynamic decision making under uncertainty. PhD Thesis. Cambridge, MA: Massachusetts Institute of Technology; 1994.
24. Cao C., Leong T., Leong A., and Seow F. Dynamic decision analysis in medicine: a data driven approach. Int J Med Informat 51 (1998) 13-28
25. Magni P. A new approach to optimal dynamic therapy planning. Proceedings of the AMIA symposium (1998), Hanley & Belfus, Philadelphia, PA 936-940
26. Magni P., Quaglini S., Marchetti M., and Barosi G. Deciding when to intervene: a Markov decision process approach. Int J Med Informat 60 (2000) 237-253
27. Dean T., and Kanazawa K. A model for reasoning about persistence and causation. Comput Intell 5 3 (1989) 142-150
28. Boutilier C., and Poole D. Computing optimal policies for partially observable decision processes using compact representations. In: Clancey W.J., and Weld D. (Eds). Proceedings of the thirteenth national conference on artificial intelligence (1996), AAAI Press, Portland, OR 1168-1175
29. Guestrin C., Koller D., and Parr R. Solving factored POMDPs with linear value functions. In: Nebel B. (Ed). IJCAI-01 workshop on planning under uncertainty and incomplete information. Seattle, Washington (2001) 65-67
30. Peek N.B. Explicit temporal models for decision-theoretic planning of clinical management. Artif Intell Med 15 (1999) 135-154
31. Hauskrecht M., and Fraser H. Planning treatment of ischemic heart disease with partially observable Markov decision processes. Artif Intell Med 18 (2000) 221-244
32. Lauritzen S.L., and Nilsson D. Representing and solving decision problems with limited information. Manag Sci 47 9 (2001) 1235-1251
33. Brown G.W. Recursive sets of rules in statistical decision processes. In: Bancroft T.A., and Brown S.A. (Eds). Statistical papers in honor of George W. Snedecor (1972), Iowa State University Press, Ames, Iowa 59-76
34. Whitehead S.D., and Ballard D.H. Learning to perceive and act by trial and error. Mach Learn 7 (1991) 45-83
35. Cooper G.F. A method for using belief networks as influence diagrams. In: Shachter R., Levitt T., Kanal L.N., and Lemmer J.F. (Eds). Proceedings of the fourth workshop on uncertainty in artificial intelligence (1988), Elsevier Science, New York, NY 55-63
36. Shachter R.D., and Peot M.A. Decision making using probabilistic inference methods. In: Dubois D., Wellman M., D'Ambrosio B., and Smets P. (Eds). Proceedings of the eight conference on uncertainty in artificial intelligence (1992), Morgan Kaufmann, San Mateo, CA 276-283
37. Dechter R., and Rish I. Directional resolution: The Davis-Putnam procedure, revisited. In: Doyle J., Sandewall E., and Torasso P. (Eds). Principles of knowledge representation and reasoning (1994), Morgan Kaufmann, San Francisco, CA 134-145
38. Murphy KP. Dynamic Bayesian networks. PhD thesis. Berkeley, CA: UC Berkeley; 2002.
39. Murphy K.P., Weiss Y., and Jordan M.I. Loopy belief propagation for approximate inference: an empirical study. In: Laskey K., and Prade H. (Eds). Proceedings of the fifteenth conference on uncertainty in artificial intelligence (1999), Morgan Kaufmann, San Francisco, CA 467-475
40. In: Doucet A., de Freitas N., and Gordon N. (Eds). Sequential Monte Carlo methods in practice (2001), Springer-Verlag, San Francisco, CA
41. Ross S. Introduction to stochastic dynamic programming (1983), Academic Press, New York, NY
42. Meuleau N., Kim K.-E., Kaelbling L.P., and Cassandra A.R. Solving POMDPs by searching the space of finite policies. In: Laskey K., and Prade H. (Eds). Proceedings of the fifteenth conference on uncertainty in artificial intelligence (1999), Morgan Kaufmann, San Francisco, CA 417-426
43. Ng A.Y., and Jordan M.I. PEGASUS: a policy search method for large MDPs and POMDPs. In: Boutilier C., and Goldszmidt M. (Eds). Proceedings of the sixteenth conference on uncertainty in artificial intelligence (2000), Morgan Kaufmann, San Francisco, CA 406-415
44. Kirkpatrick S., Gelatt C.D., and Vecchi Jr. M.P. Optimization by simulated annealing. Science 220 (1983) 671-680
45. Eglese R.W. Simulated annealing: a tool for operational research. Eur J Oper Res 46 (1990) 271-281
46. Modlin I.M., Kidd M., Latich I., Zikusoka M.N., and Shapiro M.D. Current status of gastrointestinal carcinoids. Gastroenterology 128 (2005) 1717-1751
47. Capella C., Heitz P.U., Hofler H., Solcia E., and Kloppel G. Revised classification of neuroendocrine tumours of the lung, pancreas and gut. Virchows Archiv 425 6 (1995) 547-560
48. Moertel C.G., Kvols L.K., O'Connell M.J., and Rubin J. Treatment of neuroendocrine carcinomas with combined etoposide and cisplatin. Evidence of major therapeutic activity in the anaplastic variants of these neoplasms. Cancer 68 2 (1991) 227-232
49. 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
50. Weinstein M., and Stason W. Foundations of cost-effectiveness analysis for health and medical practices. New Engl J Med 296 13 (1977) 716-721
51. Drummond M.F., Sculpher M.J., Torrance G.W., O'Brien B.J., and Stoddart G.L. Methods for the economic evaluation of health care programmes. 3rd ed. (2005), Oxford University Press, Oxford, UK
52. Kaplan R.M., and Anderson J.P. A general health policy model: update and applications. Health Serv Manag Res 23 2 (1988) 203-235
53. Haddix A., Teutsch S., Shaffer P., and Dunet D. Prevention effectiveness: a guide to decision analysis and economic evaluation (1996), Oxford University Press, Oxford, UK
54. van Gerven M.A.J., and Díez F.J. Selecting strategies for infinite-horizon dynamic LIMIDs. In: Studený M., and Vomlel J. (Eds). Proceedings of the third European workshop on probabilistic graphical models (2006), Action M Agency, Prague, Czech Republic 131-138