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Reinforcement learning design for ca...

Zhao, Yufan.

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Reinforcement learning design for cancer clinical trials.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Reinforcement learning design for cancer clinical trials./
作者:	Zhao, Yufan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2009,
面頁冊數:	119 p.
附註:	Source: Dissertation Abstracts International, Volume: 70-07, Section: B, page: 3862.
Contained By:	Dissertation Abstracts International70-07B.
標題:	Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3366451
ISBN:	9781109277180

Reinforcement learning design for cancer clinical trials.
Zhao, Yufan.

Reinforcement learning design for cancer clinical trials. - Ann Arbor : ProQuest Dissertations & Theses, 2009 - 119 p.

Source: Dissertation Abstracts International, Volume: 70-07, Section: B, page: 3862.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2009.

There has been significant recent research activity in developing therapies that are tailored to each individual. Finding such therapies in treatment settings involving multiple decision times is a major challenge. In this dissertation, we develop reinforcement learning trials for discovering these optimal regimens for life-threatening diseases such as cancer. A temporal-difference learning method called Q-learning is utilized which involves learning an optimal policy from a single training set of finite longitudinal patient trajectories. Approximating the Q-function with time-indexed parameters can be achieved by using support vector regression or extremely randomized trees. Within this framework, we demonstrate that the procedure can extract optimal strategies directly from clinical data without relying on the identification of any accurate mathematical models, unlike approaches based on adaptive design. We show that reinforcement learning has tremendous potential in clinical research because it can select actions that improve outcomes by taking into account delayed effects even when the relationship between actions and outcomes is not fully known.

ISBN: 9781109277180Subjects--Topical Terms:

1002712
Biostatistics.

Reinforcement learning design for cancer clinical trials.
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520 $a There has been significant recent research activity in developing therapies that are tailored to each individual. Finding such therapies in treatment settings involving multiple decision times is a major challenge. In this dissertation, we develop reinforcement learning trials for discovering these optimal regimens for life-threatening diseases such as cancer. A temporal-difference learning method called Q-learning is utilized which involves learning an optimal policy from a single training set of finite longitudinal patient trajectories. Approximating the Q-function with time-indexed parameters can be achieved by using support vector regression or extremely randomized trees. Within this framework, we demonstrate that the procedure can extract optimal strategies directly from clinical data without relying on the identification of any accurate mathematical models, unlike approaches based on adaptive design. We show that reinforcement learning has tremendous potential in clinical research because it can select actions that improve outcomes by taking into account delayed effects even when the relationship between actions and outcomes is not fully known.
520 $a To support our claims, the methodology's practical utility is firstly illustrated in a virtual simulated clinical trial. We then apply this general strategy with significant refinements to studying and discovering optimal treatments for advanced metastatic stage IIIB/IV non-small cell lung cancer (NSCLC). In addition to the complexity of the NSCLC problem of selecting optimal compounds for first and second-line treatments based on prognostic factors, another primary scientific goal is to determine the optimal time to initiate second-line therapy, either immediately or delayed after induction therapy, yielding the longest overall survival time. We show that reinforcement learning not only successfully identifies optimal strategies for two lines of treatment from clinical data, but also reliably selects the best initial time for second-line therapy while taking into account heterogeneities of NSCLC across patients.
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