A Deep Reinforcement Computation Model for Sepsis Treatment

Abstract

Sepsis is an emergency that usually causes a high mortality rate in intensive care units. A dynamic personalized optimal treatment strategy is necessary to develop for each patient of sepsis since even the same treatment strategy has different effects on different patients, which imposes a high challenge on the treatment of sepsis. Recently, deep reinforcement learning (DRL) achieves encouraging results in discovering an adaptive individual treatment regime for sepsis. However, traditional deep reinforcement learning models have limited ability to feature learning for high-dimensional heterogeneous data, thus to limit its effectiveness in learning dynamic treatment policy for sepsis. In this work, we present a deep reinforcement computation model to deduce dynamic treatment strategy for septic patients. The presented model combines the stacked tensor auto-encoders with Q-learning to improve DRL for high-dimensional heterogenous data learning. Furthermore, we present a training approach to update the parameters of the deep reinforcement computation model using the experience replay strategy. Finally, we verify the treatment policies learned by the presented model on a simulated set of patients by comparing with the policies given by sepsis specialists and learned by deep reinforcement learning models. The results justify that the presented model could learn the considerable level of sepsis experts in dynamic treatment policies and thus reduce mortality rate significantly on simulated patients. Therefore, the presented model is promising to contribute to smart medicine by providing the computer-aided dynamic treatment regimes for sepsis in intensive care units.

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