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Dynamic, convex, and robust optimiza...
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University of Washington.
Dynamic, convex, and robust optimization with Bayesian learning for response-guided dosing.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Dynamic, convex, and robust optimization with Bayesian learning for response-guided dosing./
作者:
Kotas, Jakob.
面頁冊數:
1 online resource (167 pages)
附註:
Source: Dissertation Abstracts International, Volume: 78-01(E), Section: B.
Contained By:
Dissertation Abstracts International78-01B(E).
標題:
Applied mathematics. -
電子資源:
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
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