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

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

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This paper focuses on term-status pair extraction from medical dialogues(MD-TSPE),which is essential in diagnosis dia-logue 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 un-derstanding 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 com-plete 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 en-riches 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-re-source settings.

Medical dialogue understandinginformation extractiontext generationknowledge-enhanced promptlow-resource settingdata augmentation

Zefa Hu、Ziyi Ni、Jing Shi、Shuang Xu、Bo Xu

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School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China

Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China

Key Research Program of the Chinese Academy of SciencesNational Natural Science Foundation of China

ZDBS-SSW-JSC00662206294

2024

10.1007/s11633-023-1461-5

机器智能研究(英文)
中国科学院自动化所

机器智能研究(英文)

CSTPCDEI
影响因子：0.49
ISSN：2731-538X
年,卷(期)：2024.21(1)
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