Abstract-Adverse reaction caused by drugs is a potentially dangerous problem which may lead to mortality and morbidity in patients. Adverse Drug Event (ADE) extraction is a significant problem in biomedical research. We model ADE extraction as a Question-Answering problem and take inspiration from Machine Reading Comprehension (MRC) literature, to design our model. Our objective in designing such a model, is to exploit the local linguistic context in clinical text and enable intrasequence interaction, in order to jointly learn to classify drug and disease entities, and to extract adverse reactions caused by a given drug. Our model makes use of a self-attention mechanism to facilitate intra-sequence interaction in a text sequence. This enables us to visualize and understand how the network makes use of the local and wider context for classification.
Ramamoorthy and Murugan REF used a self-attention mechanism to boost intra-sequence interaction in a text sequence, which enabled them to learn the dependence of classifier on different segments of the text sequence.
An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text
ArXiv
ArXiv
Computer Science