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Partially observable Markov decision model for the treatment of early Prostate Cancer
Prostate cancer is second only to lung cancer as the leading cause of cancer deaths in the world Furthermore, policies are difficult to make because of the generally indolent nature of prostate cancer and because it tends to occur in older men who often have multiple, competing medical illnesses. In...
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| Published in: | Opsearch 2010-06, Vol.47 (2), p.105-117 |
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| Main Authors: | , |
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| Language: | English |
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| creator | Goulionis, John E. Koutsiumaris, B. K. |
| description | Prostate cancer is second only to lung cancer as the leading cause of cancer deaths in the world Furthermore, policies are difficult to make because of the generally indolent nature of prostate cancer and because it tends to occur in older men who often have multiple, competing medical illnesses. In this paper we applied a Partially observable Markov decision processes (POMDP) formulation to the problem of treating patients with Early prostate Cancer (EPC). The purpose of this paper is to address the challenge of effectively managing Early Prostate cancer therapies. To solve this problem we used a procedure that take advantage of special problem structure, and we provide optimal policies to stochastic and dynamic decisions naturally arise in finding optimal disease treatment plans. |
| doi_str_mv | 10.1007/s12597-010-0015-0 |
| format | article |
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| ispartof | Opsearch, 2010-06, Vol.47 (2), p.105-117 |
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| language | eng |
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| source | ABI/INFORM Global (ProQuest); Springer Nature |
| subjects | Biopsy Business and Management Cancer therapies Decision making Decision making models Health care policy Inventory control Length of stay Lung cancer Management Markov analysis Mathematics Medical treatment Mortality Operations Research/Decision Theory Production planning Prostate cancer Radiation therapy Stochastic control theory Studies Theory and Methodology Tumors |
| title | Partially observable Markov decision model for the treatment of early Prostate Cancer |
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