An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text
This addresses a critical problem in biomedical research for healthcare professionals by providing a more interpretable method for extracting adverse drug events from clinical text, though it appears incremental as it builds on existing Machine Reading Comprehension techniques.
The paper tackled Adverse Drug Event extraction from biomedical text by modeling it as a Question-Answering problem using a self-attention mechanism, achieving joint classification of drug and disease entities and extraction of adverse reactions with improved interpretability through context visualization.
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 intra-sequence 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.