KeMRE: Knowledge-enhanced medical relation extraction for Chinese medicine instructions.

Journal of Biomedical Informatics
Tao QiYongfeng Huang

Abstract

Medicine instructions usually contain rich medical relations, and extracting them is very helpful for many downstream tasks such as medicine knowledge graph construction and medicine side-effect prediction. Existing relation extraction (RE) methods usually predict relations between entities from their contexts and do not consider medical knowledge. However, understanding a part of medical relations may need some expert knowledge in the medical field, making it challenging for existing methods to achieve satisfying performances of medical RE. In this paper, we propose a knowledge-enhanced framework for medical RE, which can exploit medical knowledge of medicines to better conduct medical RE on Chinese medicine instructions. We first propose a BERT-CNN-LSTM based framework for text modeling and learn representations of characters from their contexts. Then we learn representations of each entity by aggregating representations of their characters. Besides, we propose a CNN-LSTM based framework for entity modeling and learn entity representations from their relatedness. In addition, there are usually many different instructions for the same medicine, which usually share general knowledge on this medicine. Thus, to obtain medical kno...Continue Reading

References

Oct 23, 1997·Neural Computation·S Hochreiter, J Schmidhuber
Jun 26, 2007·Patient Education and Counseling·Michael S WolfRuth M Parker

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