  This paper focuses on term-status pair extraction from medical dialogues
(MD-TSPE), which is essential in diagnosis dialogue systems and the automatic
scribe of electronic medical records (EMRs). In the past few years, works on
MD-TSPE have attracted increasing research attention, especially after the
remarkable progress made by generative methods. However, these generative
methods output a whole sequence consisting of term-status pairs in one stage
and ignore integrating prior knowledge, which demands a deeper understanding to
model the relationship between terms and infer the status of each term. This
paper presents a knowledge-enhanced two-stage generative framework (KTGF) to
address the above challenges. Using task-specific prompts, we employ a single
model to complete the MD-TSPE through two phases in a unified generative form:
we generate all terms the first and then generate the status of each generated
term. In this way, the relationship between terms can be learned more
effectively from the sequence containing only terms in the first phase, and our
designed knowledge-enhanced prompt in the second phase can leverage the
category and status candidates of the generated term for status generation.
Furthermore, our proposed special status "not mentioned" makes more terms
available and enriches the training data in the second phase, which is critical
in the low-resource setting. The experiments on the Chunyu and CMDD datasets
show that the proposed method achieves superior results compared to the
state-of-the-art models in the full training and low-resource settings.
