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Volume 25 (2025)
Volume 25, Issue 1 (In Progress)
Pg 1 - 144 (March 2025)
Volume 24 (2024)
Volume 24, Issue 3
Pg 399 - 572 (November 2024)
Volume 24, Issue 2
Pg 197 - 397 (July 2024)
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Pg 1 - 196 (March 2024)
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Pg 1 - 94 (March 2023)
Volume 22 (2022)
Volume 22,
Pg 1 - 84 (December 2022)
Volume 21 (2022)
Volume 21,
Pg 1 - 154 (September 2022)
Volume 20 (2022)
Volume 20,
Pg 1 - 123 (June 2022)
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Volume 19,
Pg 1 - 144 (March 2022)
Volume 18 (2021)
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Pg 305 - 504 (December 2021)
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Pg 149 - 303 (August 2021)
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Pg 1 - 147 (April 2021)
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Pg 307 - 602 (December 2020)
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Pg 1 - 305 (June 2020)
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Volume 16, Issue 2
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Pg 1 - 111 (June 2019)
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Pg 83 - 173 (December 2018)
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Pg 1 - 82 (June 2018)
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Pg 1 - 48 (August 2013)
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Pg 1 - 66 (February 2013)
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Pg 107 - 212 (June 2010)
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Pg 77 - 169 (June 2009)
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Pg 1 - 75 (February 2009)
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Pg 169 - 261 (October 2008)
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JP Journal of Biostatistics
JP Journal of Biostatistics
Volume 4, Issue 2, Pages 139 - 160 (June 2010)
PLANNING IN UNCERTAIN MULTIAGENT SETTINGS FOR THE HEALTHCARE MANAGEMENT OF PARKINSON’S DISEASE
John E. Goulionis, D. J. Stengos and G. Tzavelas
Abstract:
The planning of clinical management requires the ability to predict the interplay between the natural history of diseases and effects of intervening actions over time. Often, such predictions cannot be made with certainty and trade-offs have to be made between the expected benefits of current and future decisions. We have shown how the model of partially observable Markov decision processes (POMDPs) can be used to formalize the management of patients with Parkinson’s disease, providing an explicit representation of clinical states, the management strategy employed and the objectives of treatment. Because the treatment of Parkinson’s disease should not be taken as an isolated event with one agent, we extend the POMDP model for the cases when an agent needs to plan a course of action in an environment populated by other agents. The line of work presented here opens a wide area of future research in integrating frameworks for sequential planning with elements of game theory and Bayesian learning in interactive settings. We present assumptions, some basic properties and provide a statistical technique of estimating the parameters of the POMDP model. We give a solution method for the treatment of patients with Parkinson’s disease by interacting doctors using the criterion of total discounted cost. Finally, we provide a key-study with two autonomous doctors interacting with each other in an uncertain environment.
Keywords and phrases:
operation research in clinical management, decision making tools, Parkinson’s disease, POMDP, EM algorithm, log-likelihood ratio tests.
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P-ISSN: 0973-5143
Journal Stats
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