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

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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.

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  1. https://github.com/songyouwei/ABSA-PyTorch

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Funding

This work was supported by the Russian Science Foundation, project no. 18-11-00284.

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Authors and Affiliations

  1. Kazan Federal University, 420008, Kazan, Russia

    I. S. Alimova & E. V. Tutubalina

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  1. I. S. Alimova
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  2. E. V. Tutubalina
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Correspondence to I. S. Alimova or E. V. Tutubalina.

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Translated by A. Klimontovich

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Alimova, I.S., Tutubalina, E.V. Entity-Level Classification of Adverse Drug Reaction: A Comparative Analysis of Neural Network Models. Program Comput Soft 45, 439–447 (2019). https://doi.org/10.1134/S0361768819080024

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