Improving RNN with Attention and Embedding for Adverse Drug Reactions

Abstract

Electronic Health Records (EHR) narratives are a rich source of information, embedding high-resolution information of value to secondary research use. However, because the EHRs are mostly in natural language free-text and highly ambiguity-ridden, many natural language processing algorithms have been devised around them to extract meaningful structured information about clinical entities. The performance of the algorithms however, largely varies depending on the training dataset as well as the effectiveness of the use of background knowledge to steer the learning process.

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