  Medical automatic diagnosis aims to imitate human doctors in real-world
diagnostic processes and to achieve accurate diagnoses by interacting with the
patients. The task is formulated as a sequential decision-making problem with a
series of symptom inquiring steps and the final diagnosis. Recent research has
studied incorporating reinforcement learning for symptom inquiring and
classification techniques for disease diagnosis, respectively. However, studies
on efficiently and effectively combining the two procedures are still lacking.
To address this issue, we devise an adaptive mechanism to align reinforcement
learning and classification methods using distribution entropy as the medium.
Additionally, we created a new dataset for patient simulation to address the
lacking of large-scale evaluation benchmarks. The dataset is extracted from the
MedlinePlus knowledge base and contains significantly more diseases and more
comprehensive symptoms and examination information than existing datasets.
Experimental evaluation shows that our method outperforms three current
state-of-the-art methods on different datasets by achieving higher medical
diagnosis accuracy with fewer inquiring turns.
