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This thesis explores the application of Reinforcement Learning (RL) methods in healthcare to evaluate treatment strategies. The study begins with an overview of RL, introducing key algorithms such as Q-Learning and Dyna-Q, and discusses Hidden Markov Mixture Models (HMMMs) for extracting patient subgroups from service utilization data. Finally, it proposes a pipeline for integrating these models to assess healthcare policies in an offline setting.
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