Effective Medical Test Suggestions Using Deep Reinforcement Learning
This addresses the need for better diagnostic tools in healthcare to save time and enhance accuracy, though it appears incremental as it builds on existing reinforcement learning methods.
The paper tackles the problem of improving disease diagnosis accuracy by using deep reinforcement learning to suggest effective medical tests, resulting in significant accuracy improvements.
Effective medical test suggestions benefit both patients and physicians to conserve time and improve diagnosis accuracy. In this work, we show that an agent can learn to suggest effective medical tests. We formulate the problem as a stage-wise Markov decision process and propose a reinforcement learning method to train the agent. We introduce a new representation of multiple action policy along with the training method of the proposed representation. Furthermore, a new exploration scheme is proposed to accelerate the learning of disease distributions. Our experimental results demonstrate that the accuracy of disease diagnosis can be significantly improved with good medical test suggestions.