  Motivation: Disease diagnosis oriented dialogue system models the interactive
consultation procedure as Markov Decision Process and reinforcement learning
algorithms are used to solve the problem. Existing approaches usually employ a
flat policy structure that treat all symptoms and diseases equally for action
making. This strategy works well in the simple scenario when the action space
is small, however, its efficiency will be challenged in the real environment.
Inspired by the offline consultation process, we propose to integrate a
hierarchical policy structure of two levels into the dialogue systemfor policy
learning. The high-level policy consists of amastermodel that is responsible
for triggering a low-levelmodel, the lowlevel policy consists of several
symptom checkers and a disease classifier. The proposed policy structure is
capable to deal with diagnosis problem including large number of diseases and
symptoms.
  Results: Experimental results on three real-world datasets and a synthetic
dataset demonstrate that our hierarchical framework achieves higher accuracy
and symptom recall in disease diagnosis compared with existing systems. We
construct a benchmark including datasets and implementation of existing
algorithms to encourage follow-up researches.
  Availability: The code and data is available from
https://github.com/FudanDISC/DISCOpen-MedBox-DialoDiagnosis
  Contact<EMAIL_ADDRESS>  Supplementary information: Supplementary data are available at Bioinformatics
online.
