  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.
