Entity-Level Classification of Adverse Drug Reaction: A Comparative Analysis of Neural Network Models

Abstract

An experimental work on the analysis of effectiveness of neural network models
applied to the classification of adverse drug reactions at the entity level is
described. Aspect-level sentiment analysis, which aims to determine the sentimental
class of a specific aspect conveyed in user opinions, has been actively studied for
more than 10 years. A number of neural network architectures have been proposed. Even
though the models based on these architectures have much in common, they differ in
certain components. In this paper, the applicability of the neural network models
developed for the aspect-level sentiment analysis to the problem of the
classification of adverse drug reactions is studied. Extensive experiments on English
language texts of biomedical topic, including health records, scientific literature,
and social media have been conducted. The proposed models mentioned above are
compared with one of the best model based on the support vector machine method and a
large set of features.