  Knowledge based dialogue systems have attracted increasing research interest
in diverse applications. However, for disease diagnosis, the widely used
knowledge graph is hard to represent the symptom-symptom relations and
symptom-disease relations since the edges of traditional knowledge graph are
unweighted. Most research on disease diagnosis dialogue systems highly rely on
data-driven methods and statistical features, lacking profound comprehension of
symptom-disease relations and symptom-symptom relations. To tackle this issue,
this work presents a weighted heterogeneous graph based dialogue system for
disease diagnosis. Specifically, we build a weighted heterogeneous graph based
on symptom co-occurrence and a proposed symptom frequency-inverse disease
frequency. Then this work proposes a graph based deep Q-network (Graph-DQN) for
dialogue management. By combining Graph Convolutional Network (GCN) with DQN to
learn the embeddings of diseases and symptoms from both the structural and
attribute information in the weighted heterogeneous graph, Graph-DQN could
capture the symptom-disease relations and symptom-symptom relations better.
Experimental results show that the proposed dialogue system rivals the
state-of-the-art models. More importantly, the proposed dialogue system can
complete the task with less dialogue turns and possess a better distinguishing
capability on diseases with similar symptoms.
