Enhancing unsupervised medical entity linking with multi-instance learning

doi:10.1186/s12911-021-01654-z

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 自然语言处理

	Enhancing unsupervised medical entity linking with multi-instance learning
	Yan,Cheng 1,2; Zhang,Yuanzhe 1,2; Liu,Kang 1,2; Zhao,Jun 1,2; Shi,Yafei 3; Liu,Shengping 3
刊名	BMC Medical Informatics and Decision Making
	2021-11-16
卷号	21 期号:Suppl 9
关键词	Medical entity linking Unsupervised learning Multiple instance learning
DOI	10.1186/s12911-021-01654-z
通讯作者	Zhang,Yuanzhe(yzzhang@nlpr.ia.ac.cn)
英文摘要	AbstractBackgroundA lot of medical mentions can be extracted from a huge amount of medical texts. In order to make use of these medical mentions, a prerequisite step is to link those medical mentions to a medical domain knowledge base (KB). This linkage of mention to a well-defined, unambiguous KB is a necessary part of the downstream application such as disease diagnosis and prescription of drugs. Such demand becomes more urgent in colloquial and informal situations like online medical consultation, where the medical language is more casual and vaguer. In this article, we propose an unsupervised method to link the Chinese medical symptom mentions to the ICD10 classification in a colloquial background.MethodsWe propose an unsupervised entity linking model using multi-instance learning (MIL). Our approach builds on a basic unsupervised entity linking method (named BEL), which is an embedding similarity-based EL model in this paper, and uses MIL training paradigm to boost the performance of BEL. First, we construct a dataset from an unlabeled large-scale Chinese medical consultation corpus with the help of BEL. Subsequently, we use a variety of encoders to obtain the representations of mention-context and the ICD10 entities. Then the representations are fed into a ranking network to score candidate entities.ResultsWe evaluate the proposed model on the test dataset annotated by professional doctors. The evaluation results show that our method achieves 60.34% accuracy, exceeding the fundamental BEL by 1.72%.ConclusionsWe propose an unsupervised entity linking method to the entity linking in the medical domain, using MIL training manner. We annotate a test set for evaluation. The experimental results show that our model behaves better than the fundamental model BEL, and provides an insight for future research.
语种	英语
出版者	BioMed Central
WOS记录号	BMC:10.1186/S12911-021-01654-Z
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46123]
专题	模式识别国家重点实验室_自然语言处理
通讯作者	Zhang,Yuanzhe
作者单位	1.University of Chinese Academy of Sciences; School of Artificial Intelligence 2.Chinese Academy of Sciences; National Laboratory of Pattern Recognition, Institute of Automation 3.Unisound AI Technology Co., Ltd.
推荐引用方式 GB/T 7714	Yan,Cheng,Zhang,Yuanzhe,Liu,Kang,et al. Enhancing unsupervised medical entity linking with multi-instance learning[J]. BMC Medical Informatics and Decision Making,2021,21(Suppl 9).
APA	Yan,Cheng,Zhang,Yuanzhe,Liu,Kang,Zhao,Jun,Shi,Yafei,&Liu,Shengping.(2021).Enhancing unsupervised medical entity linking with multi-instance learning.BMC Medical Informatics and Decision Making,21(Suppl 9).
MLA	Yan,Cheng,et al."Enhancing unsupervised medical entity linking with multi-instance learning".BMC Medical Informatics and Decision Making 21.Suppl 9(2021).