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

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.

Identiferoai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-2449
Date14 August 2003
CreatorsPoolla, Radhika
PublisherScholar Commons
Source SetsUniversity of South Flordia
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceGraduate Theses and Dissertations
Rightsdefault

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