Automatic diagnosis has attracted increasing attention but remains
challenging due to multi-step reasoning. Recent works usually address it by
reinforcement learning methods. However, these methods show low efficiency and
require taskspecific reward functions. Considering the conversation between
doctor and patient allows doctors to probe for symptoms and make diagnoses, the
diagnosis process can be naturally seen as the generation of a sequence
including symptoms and diagnoses. Inspired by this, we reformulate automatic
diagnosis as a symptoms Sequence Generation (SG) task and propose a simple but
effective automatic Diagnosis model based on Transformer (Diaformer). We
firstly design the symptom attention framework to learn the generation of
symptom inquiry and the disease diagnosis. To alleviate the discrepancy between
sequential generation and disorder of implicit symptoms, we further design
three orderless training mechanisms. Experiments on three public datasets show
that our model outperforms baselines on disease diagnosis by 1%, 6% and 11.5%
with the highest training efficiency. Detailed analysis on symptom inquiry
prediction demonstrates that the potential of applying symptoms sequence
generation for automatic diagnosis.

通过考虑医生和病人之间的对话，我们将自动诊断重新定义为症状序列生成任务，并基于变压器（Diaformer）提出了一个简单而有效的自动诊断模型，采用症状注意机制和三种无序的训练机制来提高自动诊断的准确性。在三个公共数据集上的实验结果表明，Diaformer 模型在疾病诊断方面优于基线模型，准确率分别提高了 1%、6%、11.5%。