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This paper focuses on term-status pair extraction from medical dialogues(MD-TSPE), which is essential in diagnosis dialogue systems and the automaticscribe of electronic medical records (EMRs). In the past few years, works onMD-TSPE have attracted increasing research attention, especially after theremarkable progress made by generative methods. However, these generativemethods output a whole sequence consisting of term-status pairs in one stageand ignore integrating prior knowledge, which demands a deeper understanding tomodel the relationship between terms and infer the status of each term. Thispaper presents a knowledge-enhanced two-stage generative framework (KTGF) toaddress the above challenges. Using task-specific prompts, we employ a singlemodel 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 generatedterm. In this way, the relationship between terms can be learned moreeffectively from the sequence containing only terms in the first phase, and ourdesigned knowledge-enhanced prompt in the second phase can leverage thecategory and status candidates of the generated term for status generation.Furthermore, our proposed special status "not mentioned" makes more termsavailable and enriches the training data in the second phase, which is criticalin the low-resource setting. The experiments on the Chunyu and CMDD datasetsshow that the proposed method achieves superior results compared to thestate-of-the-art models in the full training and low-resource settings.

A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue Information Extraction