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Volume 25 (2025)
Volume 25, Issue 1 (In Progress)
Pg 1 - 15 (March 2025)
Volume 24 (2024)
Volume 24, Issue 3
Pg 399 - 554 (November 2024)
Volume 24, Issue 2
Pg 197 - 397 (July 2024)
Volume 24, Issue 1
Pg 1 - 196 (March 2024)
Volume 23 (2023)
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Pg 227 - 327 (November 2023)
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Pg 95 - 225 (July 2023)
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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)
Volume 19 (2022)
Volume 19,
Pg 1 - 144 (March 2022)
Volume 18 (2021)
Volume 18, Issue 3
Pg 305 - 504 (December 2021)
Volume 18, Issue 2
Pg 149 - 303 (August 2021)
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Pg 1 - 147 (April 2021)
Volume 17 (2020)
Volume 17, Issue 2
Pg 307 - 602 (December 2020)
Volume 17, Issue 1
Pg 1 - 305 (June 2020)
Volume 16 (2019)
Volume 16, Issue 2
Pg 1 - 158 (December 2019)
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Pg 1 - 111 (June 2019)
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Volume 15, Issue 2
Pg 83 - 173 (December 2018)
Volume 15, Issue 1
Pg 1 - 82 (June 2018)
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Volume 14, Issue 2
Pg 85 - 120 (December 2017)
Volume 14, Issue 1
Pg 1 - 84 (June 2017)
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Pg 1 - 80 (June 2015)
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Pg 1 - 88 (June 2014)
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Pg 1 - 48 (August 2013)
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Pg 67 - 118 (May 2013)
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Pg 1 - 66 (February 2013)
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Volume 8, Issue 1-2 (Aug-Nov)
Pg 1 - 77 (November 2012)
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Pg 61 - 119 (May 2012)
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Pg 1 - 59 (February 2012)
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Volume 6, Issue 2
Pg 77 - 120 (November 2011)
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Pg 1 - 75 (August 2011)
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Pg 73 - 137 (May 2011)
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Pg 1 - 71 (February 2011)
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Pg 213 - 311 (October 2010)
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Pg 107 - 212 (June 2010)
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Pg 1 - 105 (February 2010)
Volume 3 (2009)
Volume 3, Issue 3
Pg 171 - 256 (October 2009)
Volume 3, Issue 2
Pg 77 - 169 (June 2009)
Volume 3, Issue 1
Pg 1 - 75 (February 2009)
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Pg 169 - 261 (October 2008)
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Pg 81 - 168 (June 2008)
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Pg 1 - 80 (February 2008)
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Pg 217 - 306 (October 2007)
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JP Journal of Biostatistics
JP Journal of Biostatistics
Volume 3, Issue 2, Pages 77 - 97 (June 2009)
MEDICAL TREATMENTS USING PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES
John E. Goulionis (Greece)
Abstract:
Partially observable Markov decision processes (POMDPs) have recently been suggested as a suitable model to formalizing the planning of clinical patient management. The condition of a patient is a function of many unobservable physical characteristics. A POMDP is a generalization of a Markov decision process that allows for the incomplete state information. In this paper, we discuss a specialization of POMDPs that is tailored to a frequently reoccurring type of management problem, where our goal is to determine the best timing for testing and treatment decisions for patients with ischemic heart disease (IHD). We analyze the model for structural results and find that under certain assumptions conditions which guarantee an optimal policy. We develop a solution procedure utilizing these structural properties. It is described how this solution method can be used for patients with ischemic heart disease (IHD).
Keywords and phrases:
Markov decision processes in medicine, decision analysis.
Number of Downloads:
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P-ISSN: 0973-5143
Journal Stats
Publication count:
323
Citation count (Google Scholar):
424
h10-index (Google Scholar):
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h-index (Google Scholar):
10
Downloads :
83803
Views:
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Downloads/publish articles:
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Citations (Google Scholar)/publish articles:
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