An Attentive Sequence Model for Adverse Drug Event Extractionfrom Biomedical Text
TABLE II: Comparison with the state of the art
['Model', 'Entity Recognition P', 'Entity Recognition R', 'Entity Recognition F1', 'ADE Extraction P', 'ADE Extraction R', 'ADE Extraction F1']
et al. ’s Our model’s performance in Entity Recognition is slightly higher compared to the state of the art. We also present the results of our ADE extraction system. Although, in a practical setting, we could augment an end-to-end neural network with a drug lexicon, to condition the model to extract ADE’s caused by a specific drug. By constraining our model to focus on ADE’s only relevant to a particular drug, we have achieved an F1 score of 86.78, which is an improvement of approximately 15% over the state of the art.