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 presented a simple sequence model making use of a self-attention mechanism to facilitate intra-sequence interaction in a text sequence for the combined task of entity recognition and adverse drug event extraction.
An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text
ArXiv
ArXiv
Computer Science