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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Bibliographic Details
Published in:Opsearch 2010-06, Vol.47 (2), p.105-117
Main Authors: Goulionis, John E., Koutsiumaris, B. K.
Format: Article
Language:English
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
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Summary: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.
ISSN:0030-3887
0975-0320
DOI:10.1007/s12597-010-0015-0