  Diagnosis-oriented dialogue system queries the patient's health condition and
makes predictions about possible diseases through continuous interaction with
the patient. A few studies use reinforcement learning (RL) to learn the optimal
policy from the joint action space of symptoms and diseases. However, existing
RL (or Non-RL) methods cannot achieve sufficiently good prediction accuracy,
still far from its upper limit. To address the problem, we propose a decoupled
automatic diagnostic framework DxFormer, which divides the diagnosis process
into two steps: symptom inquiry and disease diagnosis, where the transition
from symptom inquiry to disease diagnosis is explicitly determined by the
stopping criteria. In DxFormer, we treat each symptom as a token, and formalize
the symptom inquiry and disease diagnosis to a language generation model and a
sequence classification model respectively. We use the inverted version of
Transformer, i.e., the decoder-encoder structure, to learn the representation
of symptoms by jointly optimizing the reinforce reward and cross entropy loss.
Extensive experiments on three public real-world datasets prove that our
proposed model can effectively learn doctors' clinical experience and achieve
the state-of-the-art results in terms of symptom recall and diagnostic
accuracy.
