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.

提出了 DxFormer 自动诊断框架，将诊断分为症状查询和疾病诊断两个步骤，并使用逆变换器来学习症状表示，通过在现实世界数据集上进行实验得出了 DxFormer 可以有效地学习医生临床经验并在症状检测和诊断准确性方面实现了最新结果。