Ying Liu, Brent Logan, Ning Liu, Zhiyuan Xu, Jian Tang, Yanzhi Wang
In this paper, we propose the first deep reinforcement learning framework to estimate the optimal Dynamic Treatment 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 provide doctor and patients the data-driven personalized decision recommendations. The proposed deep reinforcement learning framework contains a supervised learning step to predict the most possible expert actions; and a deep reinforcement learning step to estimate the long term value function of Dynamic Treatment Regimes...
August 2017: Healthcare Informatics: the Business Magazine for Information and Communication Systems