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In this paper, we propose the first deep reinforce-ment learning framework to estimate the optimal Dynamic Treat-ment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real life complexity in heterogeneous disease progression and treatment choices, with the goal to...
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