Unsupervised Medical Entity Recognition and Linking in Chinese Online Medical Text

Journal of Healthcare Engineering
Jing XuQuanyuan Wu

Abstract

Online medical text is full of references to medical entities (MEs), which are valuable in many applications, including medical knowledge-based (KB) construction, decision support systems, and the treatment of diseases. However, the diverse and ambiguous nature of the surface forms gives rise to a great difficulty for ME identification. Many existing solutions have focused on supervised approaches, which are often task-dependent. In other words, applying them to different kinds of corpora or identifying new entity categories requires major effort in data annotation and feature definition. In this paper, we propose unMERL, an unsupervised framework for recognizing and linking medical entities mentioned in Chinese online medical text. For ME recognition, unMERL first exploits a knowledge-driven approach to extract candidate entities from free text. Then, the categories of the candidate entities are determined using a distributed semantic-based approach. For ME linking, we propose a collaborative inference approach which takes full advantage of heterogenous entity knowledge and unstructured information in KB. Experimental results on real corpora demonstrate significant benefits compared to recent approaches with respect to both ME...Continue Reading

References

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Oct 27, 2017·Journal of Healthcare Engineering·Jun LiangJianbo Lei

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Citations

Apr 25, 2019·Computational and Mathematical Methods in Medicine·Voldemaras ŽitkusMarcin Woźniak
Sep 11, 2020·Journal of the American Medical Informatics Association : JAMIA·Yonatan BittonNoémie Elhadad

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Software Mentioned

HanLP
unMEL
MetaMap
unMER
Dic
MedLEE
SNOMED
CRF
Baidu Search
ME

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