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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

A Reinforcement Learning Approach To Obtain Treatment Strategies In Sequential Medical Decision Problems

Poolla, Radhika 14 August 2003 (has links)
Medical decision problems are extremely complex owing to their dynamic nature, large number of variable factors, and the associated uncertainty. Decision support technology entered the medical field long after other areas such as the airline industry and the manufacturing industry. Yet, it is rapidly becoming an indispensable tool in medical decision making problems including the class of sequential decision problems. In these problems, physicians decide on a treatment plan that optimizes a benefit measure such as the treatment cost, and the quality of life of the patient. The last decade saw the emergence of many decision support applications in medicine. However, the existing models have limited applications to decision problems with very few states and actions. An urgent need is being felt by the medical research community to expand the applications to more complex dynamic problems with large state and action spaces. This thesis proposes a methodology which models the class of sequential medical decision problems as a Markov decision process, and solves the model using a simulation based reinforcement learning (RL) algorithm. Such a methodology is capable of obtaining near optimal treatment strategies for problems with large state and action spaces. This methodology overcomes, to a large extent, the computational complexity of the value-iteration and policy-iteration algorithms of dynamic programming. An average reward reinforcement-learning algorithm is developed. The algorithm is applied on a sample problem of treating hereditary spherocytosis. The application demonstrates the ability of the proposed methodology to obtain effective treatment strategies for sequential medical decision problems.
2

A reinforcement learning approach to obtain treatment strategies in sequential medical decision problems [electronic resource] / by Radhika Poolla.

Poolla, Radhika. January 2003 (has links)
Title from PDF of title page. / Document formatted into pages; contains 104 pages. / Thesis (M.S.I.E.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: Medical decision problems are extremely complex owing to their dynamic nature, large number of variable factors, and the associated uncertainty. Decision support technology entered the medical field long after other areas such as the airline industry and the manufacturing industry. Yet, it is rapidly becoming an indispensable tool in medical decision making problems including the class of sequential decision problems. In these problems, physicians decide on a treatment plan that optimizes a benefit measure such as the treatment cost, and the quality of life of the patient. The last decade saw the emergence of many decision support applications in medicine. However, the existing models have limited applications to decision problems with very few states and actions. An urgent need is being felt by the medical research community to expand the applications to more complex dynamic problems with large state and action spaces. / ABSTRACT: This thesis proposes a methodology which models the class of sequential medical decision problems as a Markov decision process, and solves the model using a simulation based reinforcement learning (RL) algorithm. Such a methodology is capable of obtaining near optimal treatment strategies for problems with large state and action spaces. This methodology overcomes, to a large extent, the computational complexity of the value-iteration and policy-iteration algorithms of dynamic programming. An average reward reinforcement-learning algorithm is developed. The algorithm is applied on a sample problem of treating hereditary spherocytosis. The application demonstrates the ability of the proposed methodology to obtain effective treatment strategies for sequential medical decision problems. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

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