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Dynamic, convex, and robust optimization with Bayesian learning for response-guided dosing.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Dynamic, convex, and robust optimization with Bayesian learning for response-guided dosing./
Author:	Kotas, Jakob.
Description:	1 online resource (167 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-01(E), Section: B.
Contained By:	Dissertation Abstracts International78-01B(E).
Subject:	Applied mathematics. -
Online resource:	click for full text (PQDT)
ISBN:	9781339944265

Dynamic, convex, and robust optimization with Bayesian learning for response-guided dosing.
Kotas, Jakob.

Dynamic, convex, and robust optimization with Bayesian learning for response-guided dosing. - 1 online resource (167 pages)

Source: Dissertation Abstracts International, Volume: 78-01(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Medical treatment commonly involves the administration of drug doses at multiple time-points. Intuitively, the higher the doses, the higher the likelihood of disease control as well as the risk of adverse effects and of logistical inconvenience. Since an individual patient's response to treatment is uncertain, the need to effectively balance this trade-off pervades all of medicine. In response-guided dosing (RGD), the goal is to adaptively tailor doses to each individual patient's stochastic evolution of disease condition over multiple treatment sessions. Several clinical experts, in editorial and review papers, have commented that despite a strong surge of interest in RGD, a quantitative, dynamic decision-making framework has been missing. The research objective of this dissertation is to apply stochastic dynamic programming (DP), convex optimization, and Bayesian learning methods to develop such a mathematically rigorous framework to facilitate dosing decisions in RGD. The ultimate goal of this framework is to administer the right dose to the right patient at the right time.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781339944265Subjects--Topical Terms:

1069907
Applied mathematics.
Index Terms--Genre/Form:

554714
Electronic books.

Dynamic, convex, and robust optimization with Bayesian learning for response-guided dosing.
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500 $a Adviser: Archis Ghate.
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504 $a Includes bibliographical references
520 $a Medical treatment commonly involves the administration of drug doses at multiple time-points. Intuitively, the higher the doses, the higher the likelihood of disease control as well as the risk of adverse effects and of logistical inconvenience. Since an individual patient's response to treatment is uncertain, the need to effectively balance this trade-off pervades all of medicine. In response-guided dosing (RGD), the goal is to adaptively tailor doses to each individual patient's stochastic evolution of disease condition over multiple treatment sessions. Several clinical experts, in editorial and review papers, have commented that despite a strong surge of interest in RGD, a quantitative, dynamic decision-making framework has been missing. The research objective of this dissertation is to apply stochastic dynamic programming (DP), convex optimization, and Bayesian learning methods to develop such a mathematically rigorous framework to facilitate dosing decisions in RGD. The ultimate goal of this framework is to administer the right dose to the right patient at the right time.
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538 $a Mode of access: World Wide Web
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650 4 $a Operations research. $3 573517
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