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

doi:10.1007/s11633-023-1461-5

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > International Journal of Automation and Computing

	A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue Information Extraction
	Zefa Hu 1,2
刊名	Machine Intelligence Research
	2024
卷号	21 期号:1 页码:153-168
关键词	Medical dialogue understanding, information extraction, text generation, knowledge-enhanced prompt, low-resource setting, data augmentation
ISSN号	2731-538X
DOI	10.1007/s11633-023-1461-5
英文摘要	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.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54580]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 2.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
推荐引用方式 GB/T 7714	Zefa Hu. A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue Information Extraction[J]. Machine Intelligence Research,2024,21(1):153-168.
APA	Zefa Hu.(2024).A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue Information Extraction.Machine Intelligence Research,21(1),153-168.
MLA	Zefa Hu."A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue Information Extraction".Machine Intelligence Research 21.1(2024):153-168.